Primary Position Table for MySQL (a.k.a. the lamest Christmas present you will ever receive)

As indicated by the notice in the margin, my computer died recently, so I have been rebuilding my databases the past few days. While I was at it, I figured this was as good a time as any to finally build a primary position table rather than keep writing various position requisites into each query whenever I need it. I also figured this may be of use to someone else as well, so I'm publishing the query I wrote here as my Christmas gift to you (if you would like to use this query but do not celebrate Christmas, feel free to classify this gift accordingly; if you would like something a little less esoteric for Christmas, well, that's what family and friends are for).

Merry Christmas (link to query text)

A few notes on this query:

• It is written for MySQL. I don't know how well it would adapt to other programs.


• It is written for use with the Baseball-Databank. bbdatabank is the name of my database; if you have named yours something different, change occurrences of "bbdatabank.*" accordingly.


• This query creates a table that lists each defensive position played by a player and indicates whether the position is that player's primary position. The line "CREATE TABLE bbdatabank.prim_pos AS" (line 10) is currently commented so that the query will not create the table but display the results instead. You can run it as is to check the output, or you can un-comment that line (delete the "#" in front of it) to create the table in the bbdatabank database.


• I classified both corner OF spots as one position (for example, Ty Cobb's primary position is listed as CF, while Hank Aaron's is corner OF). I do this because if a player has 100 games each at LF and RF, and 105 G at CF, I consider him primarily a corner OF and not a center fielder, but you can change this with some minor tweaks to the code if you want.


• For years prior to and including 1955, OF games are broken down by position in a separate table of the Baseball-Databank db, so to get games split out between LF/RF/CF, you need to use two different tables to get defensive games. However, 1954 and 1955 have OF games split out by individual position in the main fielding table as well, so you need to exclude those years from one table or the other to avoid double counting. I chose to just use the fieldingOF table in its entirety and cut 1954-55 from the main fielding table, but you can switch that if you want. There are some minor discrepancies between the two tables, but it shouldn't make a huge difference (at least not from any of the entries I have looked at).


• I apologize if the query looks much longer and nastier than it needs to. It could probably be written more concisely, especially if I used joins, but I try to avoid using joins on large tables like this if I can help it, because using unions and variables to do the same thing runs much, much faster on my computer. Speaking of which, this query actually has multiple statements that need to be run (the various statements at the beginning that set the initial variable values, and then the one long query), so it would be useful to run this as a script. That is not a problem in the new MySQL Workbench or the command line, but if you are still using the old Query Browser, that might not work. If you are using the old Query Browser, all you have to do is run each of those SET statements on its own before running the main query (all in the same tab). I haven't tested the query without running those statements first to check whether the results are still accurate, so I guess it might work anyway, but I strongly recommend using them.

The method for determining a player's primary position has two steps. First, each position is categorized as follows:

-P: Pithcer
-C: Catcher
-off_IF: 1B, DH (I know DH is not an infield position; I just wanted to classify these two positions together, and this is what I called it. You can change it if it bothers you)
-def_IF: 2B, 3B, SS
-OF: cOF, CF (remember that I combine LF and RF into cOF)

I total a player's games in each category and used that to determine a player's primary category. Then, I looked at the number of games at each position in each category and determine the primary position within each category. The primary position in the primary category is the player's overall primary position. For example, let's look at Willie Bloomquist:

DH- 21 G; 1B-21 G; 2B-106 G; 3B- 121 G; SS-150 G; CF-186 G; cOF-37 G

That gives us category totals of:

P: 0
C: 0
off_IF: 58
def_IF: 377
OF: 295

Here, we see that Willie's primary category is def_IF. That means his primary position has to come from this category. Of those positions (2B, 3B, SS), he has the most games as SS, so that is his primary position, even though he has more games at cOF than at SS.

The fields generated for this table are:

• playerID


• prim_pos: 0 if the position is not the player's primary position, 1 if it is


• pos: defensive position


• cat: the category the position is classified in, as described above


• G: defensive games


• tot: combined defensive games at all positions


• cat_tot: combined defensive games at all positions in that category


• maxP: 0 if the position is not the primary position for that category (Pos with max games in that category only), 1 if it is


• prim_cat: 0 if the category is not the primary category, 1 if it is


• percPos: the percentage of total defensive games that are at that position (G/tot)


• percCat: the percentage of total defensive games that are in that category (cat_tot/tot)


• percP_C: the percentage of defensive games in that category that are at that position (G/cat_tot)

Hacking the Hack:

Splitting up cOF into LF and RF: Perhaps you don't like that I combine LF and RF into one position. Splitting them back up shouldn't be too much trouble. Where pos is defined as "if(pos='LF','cOF',if(pos='RF','cOF',pos))", (such as in line 108, but there are other places that will need to be changed as well), change that to just "pos" (no quotes). For example:


(SELECT playerID, if(pos='LF','cOF',if(pos='RF','cOF',pos)) as pos
,sum(if(yearID<=1955 and pos regexp 'F$',0,G)) as G
,if(pos='2b' or pos='3b' or pos='ss','def_IF'
,if(pos='1b' or pos='DH','off_IF'
,if(pos='P','P' ,if(pos regexp 'F$','OF','C')))) as cat
from bbdatabank.fielding
where pos!='OF'
group by playerID, if(pos='LF','cOF',if(pos='RF','cOF',pos))

would become


(SELECT playerID, pos
,sum(if(yearID<=1955 and pos regexp 'F$',0,G)) as G
,if(pos='2b' or pos='3b' or pos='ss','def_IF'
,if(pos='1b' or pos='DH','off_IF'
,if(pos='P','P' ,if(pos regexp 'F$','OF','C')))) as cat
from bbdatabank.fielding
where pos!='OF'
group by playerID, pos

I think there are 4 instances where that has to be changed (the above two changes at lines 108 and 116, plus two more at 137/145).

Also, you will need to change the query of the fieldingOF table to split cOF into LF and RF:


UNION ALL

SELECT playerID, 'cOF' as pos, sum(Glf+Grf) as G, 'OF' as cat
from bbdatabank.fieldingof
group by playerID


should become:


UNION ALL

SELECT playerID, 'LF' as pos, sum(Glf) as G, 'OF' as cat
from bbdatabank.fieldingof
group by playerID

UNION ALL

SELECT playerID, 'RF' as pos, sum(Grf) as G, 'OF' as cat
from bbdatabank.fieldingof
group by playerID


The above change will need to be done in two places.

These changes will make it so the 100/105/100 split for OF games at LF/CF/RF selects CF as the primary position. It would also be possible to keep the corner OF designation but still specify LF or RF as a primary overall position. You would want to create a second type of category just for corner OF. You could do this by copying and pasting the appropriate part of the code and then editing the appropriate lines, but this is going to require knowing which part of the code adds the new columns for "cat" and "pos". This would probably be more straightforward if the code used joins, but it doesn't (sorry). I would actually prefer to do it this way, but I was tired of writing this query and it is not that important to me to split up LF and RF, so I didn't get to it. If I do it sometime later, I'll update the linked query text.

Changing the groupings of position categories: Don't like how I chose to group the positions? Not a problem. The section you need to change is:


if(pos='2b' or pos='3b' or pos='ss','def_IF'
,if(pos='1b' or pos='DH','off_IF'
,if(pos='P','P'
,if(pos regexp 'F$','OF','C')))) as cat


I believe this appears at lines 45, 59, 79, 110, and 139 (assuming you haven't added or removed lines editing other things, of course). For each occurrence, edit that statement to fit your liking (just make sure you edit each occurrence to the same thing). For example, you can move 1B into one IF group with 2B, 3B, and SS and leave DH as its own separate group, or group the positions into corner positions and up the middle positions. If you want to add more groups, just add a new line to the if statement just like the others, and add a close-parenthesis at the end next to the others. If you want to delete a category, make sure to also delete a close-parenthesis. The catcher category right now is just left as whatever is leftover after the other positions are assigned, but you can add a separate line for the catcher class if you want. For example, you might do this:


if(pos='2b' or pos='ss' or pos='c','middle_IF'
,if(pos='1b' or pos='3b','corner_IF'
,if(pos regexp 'F$','OF'
,if(pos='P','P'
,if(pos='DH','DH',''))))) as cat


Note that when I used an explicit if statement for each individual category (i.e. no just assigning the leftovers to a category, like I did with catchers above), I still have to end with ",''" before the close-parentheses. You need something there between the last comma and the series of close-parentheses for the query to run.

IMPORTANT: the fielding table has entries for both individual outfield positions and for combined OF totals. If you don't do something about this, OF games will be double counted, so the following line appears near each of the category definition occurrences:


where pos!='LF' and pos!='RF' and pos!='CF'


This is because I am classifying all OF positions in the same category, so I just use the OF entry and ignore the LF, CF, and RF entries. If you want to split those up (say, for example, put CF in an up the middle category with 2B and SS, and LF/RF in a corner category with 1B and 3B), then you'll have to change this to "where pos!='OF'". Bear in mind that this will not return any OF games before 1954, and you'll have to use the fieldingOF table as well, which would be sort of a pain.

Clean up the code by eliminating the need to use two separate OF tables for pre- and post-1955: Say you don't want to deal with having to combine two tables to get your OF games. You can create a new table before running this query that lists games played at each position by combining the fielding and fieldingOF tables. Then, instead of referencing those two tables in this query, just reference your new one and you won't ever have to worry about complicating your queries referencing additional tables every time you want OF games split up by position.

Utilize the percentages: You can use the percPos, percCat, and percP_C fields to further classify positions. You can create multiple primary positions or categories, classify primary positions/categories as strong or weak primaries, identify players with no true primary position, etc.

Add more types of categories: As indicated above, you could add a new type of category for corner OF, and then if cOF is the primary position, look in that category to choose between LF and RF. Additionally, you could break out DH into its own type of category, and first determine whether a player is primarily a fielder or a DH, and then if he is a fielder, look at the fielding categories to determine primary fielding category and then primary position.


Those are just some possibilities for what you could change. If you have other changes you want to make, go right ahead. Whether you want to use this as-is or just as a base to develop your own query from, as long as you find it useful (or I guess even if you don't), do whatever you want with it.

Link in case you missed it above:

https://docs.google.com/leaf?id=0B6kgiU_VMWjUYjVlNGI4YWItNjFjNy00N2EyLThjOTktMTgyNTE3Yzk5Y2U2&hl=en
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My Cy Young/MVP Ballots

I don't think I have published my personal ballots here before, but every year I put them together as part of private discussions. I finished filling out my MVP ballots today, and did my Cy Young ballots* earlier this week. I figure since I have a blog about baseball, and since the recent Cy Young result is such a hot topic, I might as well go ahead and publish them here, so here goes.

Cy Young

The Process: For my pitcher rankings, I considered the following stats for each pitcher (all from FanGraphs): ERA, RA, FIP, xFIP, tERA, WPA, and WPA/LI. Each of these was combined with IP to create a WAR figure, with ERA, RA, and FIP park-adjusted using B-R's multi-year park factors. I then averaged these 7 WAR figures to one final figure. A slightly crude process, to be sure, but if I'm going to put a lot of time into a topic, I'd rather put it into something more important than this.

The top five pitchers in each league based on my process were:

AL:
1. Felix Hernandez (7.4 WAR)
2. Jered Weaver (6.2)
3. Cliff Lee (6.0)
4. CC Sabathia (5.9)
5. Jon Lester (5.7)

NL:
1. Roy Halladay (7.6 WAR)
2. Adam Wainwright (6.7)
3. Ubaldo Jimenez (6.6)
4. Josh Johnson (6.2)
5. Roy Oswalt (5.6)



MVP

The Process: For non-pitchers, I used a combination of the batting component of fWAR (park-adjusted wRAA) and WPA/LI for offense. For defense, I used a combination of the Fan Scouting Report, UZR, and Dewan's DRS, plus the position adjustment from fWAR. The replacement bonus from fWAR was also used. Once again, all stats were from FanGraphs. Pitcher values were used from the Cy Young rankings, though when a hitter and pitcher were close, I also gave some consideration to the pitcher's offensive production in the NL (compared to other pitchers). For non-pitchers who were particularly close, I also looked at non-SB baserunning value from BaseballProspectus, though I didn't add that in systematically (purely due to inconvenience). Players I moved up or down from these adjustments are marked. I also gave some deference to non-pitchers in close decisions.


AL:
1. Josh Hamilton (7.7 WAR)
2. Felix Hernandez (7.4)
3. Jose Bautista (7.3)
4. Evan Longoria (7.1)
5. Robinson Cano (6.9)
6. Carl Crawford (6.5; plus notable baserunning value)
7. Adrian Beltre (6.6)
8. Miguel Cabrera (6.3)
9. Jered Weaver (6.2)
10. Cliff Lee (6.0)

NL:
1. Albert Pujols (7.5 WAR; baserunning moves him past Votto and Halladay)
2. Joey Votto (7.5)
3. Roy Halladay (7.6; poor hitting--even for a pitcher--from Halladay)
4. Ryan Zimmerman (7.2)
5. Troy Tulowitzki (6.7)
6. Adam Wainwright (6.7)
7. Carlos Gonzalez (6.4; baserunning)
8. Ubaldo Jimenez (6.6; also especially poor hitting)
9. Matt Holliday (6.3)
10. Josh Johnson (6.2)


This is how I would vote were I so empowered. Of minor note; I did not include any kind of league adjustment for the AL vs. NL since these lists don't have to compare between leagues, so apply that caveat when comparing players from the AL lists to the NL lists and vice versa.


*While I keep using the term "ballots" here to refer to my own personal rankings, there is really nothing ballot-like about them (since I am not actually voting for anything) other than the fact that they are mimicking real life ballots**

**Well, mimicking the fact that they are ballots, anyway. Not so much mimicking the thought or content behind them.
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On Correlation, Regression, and Bo Hart

July 4, 2003. The Cardinals were starting a weekend series against the rival Cubs. They were looking to pull ahead of Houston for the division lead and to hold off Chicago, who stood just a game back.

The Cards had come into the year with high expectations, having averaged 95 wins over the past 3 seasons, and were hoping to capture their first pennant in over 15 years. An early-season hamstring injury to second baseman and lead-off hitter Fernando Vina, however, had seemed a costly blow; doubly so when back-up Miguel Cairo also went down with injury. And yet, the Cards were still hanging tough, thanks in large part to the stellar contributions of Vina's and Cairo's replacement, Bo Hart.

Hart had come out of nowhere. A 33rd round draft pick out of Gonzaga, he had just been promoted to AAA that year at age 26 and was in his 5th year with the organization. He had put up an OPS under .700 in his one year at AA in 2002. No one wanted to have to rely on this guy for production, but he was all the Cards had at this point. Now, a couple weeks into his Major League career, he was flirting with .400.

Hart doubled in the second inning against the Cubs to push the lead to 4-0. He drew a walk and scored in the 4th, singled and scored in the 8th, and then singled in the team's final run of the game in the 9th to aid in an 11-8 victory. Now with 75 PAs, he was hitting .412 with an OBP over .450 and a SLG close to .600.

In case the four-paragraph intro about Bo-freaking-Hart playing the part of Ted Williams (or the title of the post) was too vague a tip-off, this post is about regression to the mean.

Regression to the mean is an important concept in understanding baseball statistics. It is key to answering the question of how much an observed performance (i.e. a player's stats) tells us about his underlying talent. Every player has a true talent for a given ability such as getting hits or getting on base. This true talent represents the probability of him succeeding in a given plate appearance. For example, if a player has a 33% chance of getting on base, .330 represents his true talent OBP skill.

The problem is that we don't know what the true talent probabilities are for any player. All we can do is try to infer what those probabilities might be based on each player's observed performance. In Bo Hart's case, his true talent OBP ability may well have been .300, but it is still possible to observe him performing at a .450 level, especially over small samples. If you give Bo Hart (or anyone else) one million PAs, then his observed performance will almost always be very close to his underlying ability, and those one million PAs will allow you to infer with a high level of certainty what his underlying ability is.

Over 75 PAs, however, that is not the case. His observed performance tells you something about his underlying ability, but whatever inferences you make from those 75 PAs will have much more uncertainty.

At what point can we be relatively certain about our inferences of true talent based on observed performance? 75 PAs is not enough, and one million is plenty, but what about 1000? The answer is that there is no point where our inferences become meaningful; they always have some degree of meaning and some degree of uncertainty (even at 75 PA or at one million PA), and the more PAs we observe, the more that balance slides toward having more meaning. What is important is not simply whether a stat is meaningful or not, but to describe the amount of meaning an observed performance has given its sample size.

We can do this with regression to the mean. What regression to the mean does is basically looks at an observed performance (for example, a .450 OBP over 75 PAs), and it gives us an estimate of what the hitter's most likely underlying talent is. For example, let's say that when we regress our .450 OBP performance, we get an estimate of true talent of .360. What that means is that if we see a bunch of hitters who all performed at a .450 level over 75 observed PAs, and then we keep observing the same group of hitters, then we will likely observe roughly a .360 OBP for the group as a whole outside of the initial 75 PA sample. That performance outside the initial sample is going to be pretty close to the true talent of the group (as long as you observe enough players to get a large sample, at least).

A few things to keep in mind about regression to the mean:

-The regressed estimate of true talent is always going to be closer to the league average than the observed performance is (hence the name).
-Regression toward the mean works as a percentage of the difference between the observed performance and the league average. For example, if you would regress a .450 observed OBP to .360, you might only regress a .400 observed OBP to .350; what is important is that you are regressing 75% (or however much) toward the mean, not that you are regressing .090 points toward the mean.
-While the regressed estimate is our best inference about a player's underlying talent, and while this estimate works well for groups of players, sometimes this estimate will be wrong for individual players. If we have a group of players who have a regressed estimate of .360 for their true OBP talent, then some of them will actually be .300 OBP hitters, and some of them will actually be .400 OBP hitters. However, there is no way to tell which hitters are which by looking at their observed performances; all we know is that they are likely around .360 as a whole, and that is our best guess (with some degree of uncertainty) for each hitter in that group.
-The amount of regression toward the mean depends both on the nature of the stat and of size of the observed sample.

The final point is particularly important. For a given stat, we will always observe a variety of performances across the league. If we are looking at OBPs for players league-wide, we will see some guys around .300, and some guys around .400, and all sorts of values in between, plus a handful even more extreme than that. There are two main reasons we observe varying performances: players have varying underlying talents, and the performances we observe have random variation around the players' underlying talents.

These two sources of variation in the observed performances across the league are the key to regression to the mean. Specifically, how much of the variance of observed performances comes from the variance of players' underlying talents and how much comes from random variation determines how much we need to regress a particular stat. If 50% of the observed variance is due to the variance of underlying talent and 50% is due to random variation, then you regress 50% toward the mean. If 70% of the observed variance is from the spread of underlying talent and 30% is from random variation, then you regress 30% toward the mean.

The balance between these two variances (the spread of underlying talent and random variation) will depend on both the nature of the stat you are looking at and the sample size you are looking at. Specifically, the variance in the spread of talent depends on the nature of the stat, while the variance due to random variation depends on the sample size. If the spread of true OBP talent across the league has a standard deviation of .030 points, then the spread of underlying talent follows the same distribution whether you look at 1 PA or 1000 PAs (assuming you are looking at the same group of hitters, at least). This variance does not depend on the sample size. The amount of random variation around each player's true talent, on the other hand, is purely (for the most part, anyway) a function of sample size.

Because the random variation continually drops as the sample size rises while the spread of underlying talent remains the same, it is easy to see how increasing sample sizes will affect regression to the mean: the percentage of total variation from the static distribution of underlying talent will constantly rise as the random variation continues to drop, and you will have to regress by smaller amounts. Furthermore, this illustrates how regression to the mean is dependent on the spread of talent across the league. The larger the variance in the spread of talent, the less you need to regress for a given sample size, because the random variation associated with that sample size will account for a smaller portion of the overall variance.

The trick to regression to the mean is figuring out the balance of these two variances for a given stat and sample size. This can be done with correlations. Say that you have observed several hitters over 75 PAs each and marked each hitter's OBP over those 75 PAs. Next, you are going to repeat this test; that is, you are going to observe the same hitters over another 75 PAs. The correlation between the results of the initial test (the first 75 PAs for each hitter) and the results of the second test will tell you the amount you have to regress OBP at 75 PA. If the correlation is .25, then that means the variance from the spread of talent accounts for 25% of the variance in the sample, and so you need to regress the observed results 75% toward the mean.

If you have taken statistics courses, this last part may seem off to you. There is a number that represents the percentage of variance in one variable that is explained by another variable, and it is not the correlation coefficient (which is also called "r", by the way). It is instead the coefficient of determination, which is equal to the square of the correlation coefficient (also called "r squared", oddly enough). Why, then, am I saying that the correlation coefficient tells us how much of the variance comes from the spread of talent as opposed to random variation?

The two variables we are correlating in this exercise are both observed samples, each of which contains random variation in each player's observed OBP around that player's true talent OBP. What we care about, however, is not the relationship between two separate observed samples, but the relationship between each observed sample and the distribution of underlying talent for the players in the sample. If we get a value for r between the two observed samples of .25, then that means that the variance of one sample only explains 6.25% of the variance of the other sample, but that doesn't tell us how much of the variance of each sample is explained by the variance of true talent.

Remember that each observed sample contains two sources of variance: the spread of true talent, and random variation. When we look at two identical samples, the true talent of each player is the same in both samples*. Therefore, the variance due to the spread of talent in one sample explains 100% of the variance due to the spread of talent in the other sample. Since the rest of the variance in each sample is random, it is going to be completely uncorrelated, and the random variation of one sample explains 0% of the random variation of the other sample.

We know that 6.25% of the variance of one sample is explained by the variance of the other sample, and that all of that 6.25% is from the amount of variance explained in each sample by the spread of talent. Given that, how much of the variance of each individual sample is explained by the spread of talent? The answer is the square root of .0625, which is .25. The reason is as follows.

Assume that 25% of the variance of each sample is due to the spread of talent and 75% is due to random variation. Now, how much of the variation in one sample will be explained by the variation of the other sample? First, we look at how much of the variation we need to explain is random variation. This is 75%, as per the assumption stated in this paragraph. That leaves 25% of the variation that can relate to the other sample. Only 25% of the variation of other sample is explained by non-random variation, however, so the relationship between the variances of the two samples is:

25% of the variance from one sample explains 25% of the variance of the second sample, which is equivalent to .25*.25=.0625.

This matches what we see in reality. When 6.25% of the variance of one observed sample is explained by the variance of a separate observed sample of the same players over the same number of PAs, then 25% of the variance of each sample is explained by the spread of talent across the sample, and 75% is explained by random variation. Therefore, when r is .25 between two observed samples, then the r^2 between each observed sample and the true talent of each player is also .25 (since it is just the square root of the r^2 between the observed samples), which is equal to an r of .5.

That is why it is the correlation coefficient between observed samples that we care about and not the coefficient of determination; the former, and not the latter, is what represents r^2 between each observed sample and each player's true talent, and therefore tells us how much of the variation of the observed sample is explained by non-random variation. This is what tells us how much to regress an observed performance.

Once you know how much to regress a stat for a given sample size, you can estimate the amount of regression for any sample size. You do this by figuring out how many PAs are represented by the amount of regression you need to do. If you have to regress 75% for a 75 PA sample, then that means that the 75 observed PAs need to account for 25% of the final regressed estimate. 75 is 25% of 300, which means you have to add 225 PAs of the league average to the observed performance to regress toward the mean. If you do not know the value of r for different sample sizes, then you can still use this 225 PA figure. Simply add 225 PAs of the league average for the stat you are regressing to the observed performance, no matter how many PAs are in the observed sample.

This is simple enough if you have the correlation between two observed samples of the same size. However, what if your two samples are not the same size? For example, say you have one sample where each hitter has 75 PAs, and another sample where each hitter has 150 PAs, and the correlation between the two samples is .25. How much do you regress? If you regress the 75 PAs 75%, then you can't also regress the 150 PAs 75%, because we know the amount of regression has to diminish as the sample size increases. We can't just choose either the 75 PA or the 150 PA sample to regress 75% since there is no reason to prefer one choice over the other. Rather, the 75 PA sample will be regressed something more than 75%, and the 150 PA sample will be regressed something less.

To figure out how much to regress in this situation, we should keep in mind the relationship of the correlation between the two observed samples to the correlation of each sample to true talent. When the two observed samples have the same number of PAs, the r between the two samples equals the r^2 between each sample and the true talent of the sample. When the samples are different, similar logic leads us to the conclusion that the r between the two samples is equal to the product of the r's between each sample and the true talent of the sample (note that this simplifies to r^2 when both samples have the same number of PA). For example, let's say that we already figured out the proper regressions for both 75 PA and 150 PA, and we found that at 75 PA, we regress 81%, and at 150 PA, we regress 68%. Those represent the r^2 between each sample and true talent, so to find r, we need to take the square roots of those figures:

sqrt(1-.81)=.44
sqrt(1-.68)=.57

Those are the r figures between each sample and true talent, so to find the expected r between observed samples of 75 and 150 PA, multiply those two values:

.44*.57=.25

So, if you have a correlation of .25 between observed samples of 75 and 150 PA, you would regress the 75 PA sample 81% toward the mean and the 150 PA sample 68% toward the mean. Both of these correspond to adding 313 PA of average production to the observed performance (the reason we use those figures is that that is the point where the regression component has the same weight in both regressions). Additionally, once we know that we have to add 313 PA to each sample to properly regress, we can figure out what size sample we would need to regress 75% if both samples had the same number of PAs. This ends up being about 104 PA, which means that having two samples of 75 and 150 PA that correlate at r=.25 is the same as having 104 PA in each sample. You may notice that 104 is fairly close to the harmonic mean of 75 and 150 (100), but that doesn't have to be the case if the two sample sizes are particularly far apart (for example, if the two samples were 75 PA and 1000 PA). When the correlation is close to one or when the number of PA in each sample aren't that different, then the harmonic mean should work pretty well, but as a general rule, it doesn't have to.

If you don't know the correlation coefficient for each individual sample ahead of time (which you don't if you are using this method, otherwise you wouldn't need to to this), then it is a bit trickier to figure out how to regress each sample. You have to use the quadratic formula to find the 313 PA figure, and then you can use that to figure out how much to regress. The quadratic formula is:

x = (-b + sqrt(b^2 - 4ac))/(2a)

x in this case will tell you how many PA of average to add to an observed performance to regress. To solve for x, you need to know the number of PAs in each sample, as well as the r^2 between the two samples (not r, but if you have r, just square it). Set a, b, and c as follows, and then plug them into the quadratic equation above:

a = 1
b = PA1 + PA2
c = PA1 * PA2 * (r^2-1) / r^2

Plugging in PA1=75, PA2=150, and r^2=.0625, that gives a value of x=313. Since 75 is 19% of 75+313, the observed performance of the 75 PA sample makes up 19% of the regressed value, which means you regress 81%. Same thing with the 150 PA sample to find that you regress 68%, just like we saw above.

If you have read this far, I should probably apologize for suckering you in with a nice story about Bo Hart and then turning this into a math post. I could have gone on with Bo Hart's story following that game, but honestly, it's not any prettier than a few thousands words about math, so let's just leave Bo Hart be. The important thing here is that any time you want to make conclusions about a player or team based on their observed performance, understanding how much your observed sample tells you about the player's or team's underlying ability is key to understanding what to expect going forward, and regression to the mean is a key concept to that understanding. Having a general idea of what stats regress how much over a given sample is an important to figuring out how much the stats you are looking at tell you about a player's ability. Pizza Cutter published some figures that are particularly useful here, which are summarized on FanGraphs (he presents the PA totals at which you would regress 30% toward the mean for several common offensive stats).

The relationship between r and regression to the mean is particularly important. While r^2 tells us how much variance of one variable is explained by another, the fact that we cannot directly compare our observed samples to the distribution of underlying talent and can only infer the relationship to underlying talent by comparing two observed samples means that in this case, the observed r, and not r^2, is what represents the amount of variance that is explained by underlying talent and not random variation. If we could measure true talent directly, then we would want to use r^2, but since the two variables we are correlating each include random variation around each player's true talent, knowing how much one variable explains the variation in the other doesn't tell us how the samples relate to true talent, which is what we care about for projecting performance. This is why we use r and not r^2 in determining how much to regress an observed performance toward the mean.

Further reading:
-Tangotiger's response to Pizza Cutter's work
-Phil Birnbaum on r vs. r^2
-Tangotiger/TheBookBlog community on r vs r^2 (this thread just started since I began writing this, so I am not sure where the discussion will end up)


*This article considers the simplified scenario when each player's true talent is identical in both samples. In reality, each player's true talent changes over time, and is also affected by things like park, opposing pitchers and defenses, etc. One way to minimize this problem is to select your two samples by taking the odd and even PAs over a given time period as your two samples instead of taking two samples from different time periods, so that it is virtually impossible for a player's talent to change much between your samples (though it is still possible, though less likely, for the samples to be affected by things like park and opposition). This is what Pizza Cutter did in the linked study.

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Rounding Errors, Part II (Yardage Gains in the NFL)

Last week, I wrote about measuring the effects of rounding errors. When I was working on that article, it reminded me of a question I had been wanting to look into some time ago and then forgotten about. I had gotten to thinking about how yardage gains are measured in American football, always in full-yard increments. No matter the gain, it is always recorded to the nearest full yard (i.e. a 4-yard gain or a 5-yard gain, but never a 4.5 yard gain). What I had wondered was, if a player has a few hundred plays over a season, or a few thousand over a career, how much rounding error is involved? What are the chances that a back who rushes for 990 yards in a season really gained 1000 without rounding off his individual plays, or that a passer who threw for 3010 only reached the 3000 milestone with the aid of rounding up?

I never really thought about it enough to actually sit down and figure it out at the time, but, conveniently enough, last week's installment left us fully equipped to address this type of question. We learned then that rounding errors can be thought of in terms of continuous distributions, and that when those errors are added, the resulting distribution can be described in terms of the total combined variance and standard deviation. For the rounding errors for the yardage of football plays, we can think of this distribution as a one-yard wide distribution centered on the whole number.

In other words, let's say Barry Sanders rushes for 2 yards. Whether he really gained 4 1/2 feet, or 7 1/2 feet, or anywhere in between, the NFL is just going to call it 6 feet. That 2-yard gain could fall anywhere on the continuous spectrum from 1.5 yards to 2.5 yards, and when we see a 2-yard gain recorded in the data, we have no idea where in that spectrum the gain falls. That is a continuous distribution, and the standard deviation for the rounding error of this play is .289 yards.

Before we go on, I'd like to highlight some of the assumptions we are making here when we choose a continuous distribution that is one-yard wide. One, we are assuming every gain is properly rounded to the nearest whole yard. In reality, maybe the ref spots the ball 4.4 yards from the line of scrimmage and the scorer eyeballs that and sees it as a 5 yard gain, or the ball is spotted for a 4.6 yard gain and the scorer marks it as a 4 yard gain. Because of this, when you see a two yard gain, the distribution of possible gains will actually be a bit wider than 1 yard, and it won't be continuous, but slope downward toward the ends. However, we'll ignore that so that we can mathematically describe the situation. Another thing being ignored is the distribution of gains on all plays in the NFL, or for a given player. If more runs go for 2 yards than 1 yard, and more runs go for 3 runs than 2 yards (I am making this up; I have no idea if this is how rushing gains are distributed in the NFL or not), then when you see a 2-yard gain, it is more likely to be rounded down than rounded up. That won't give us a continuous distribution either. But, for our purposes, we are going to assume we know nothing about how gains are distributed (hey, I actually don't know that!) and act under the assumption that there is no reason to believe any number in the spectrum from 1.5 yards to 2.5 yards is any more likely than another.

Now that that is out of the way, let's continue. The SD for the rounding error of each play is .289 yards. Now, let's say Barry Sanders rushes again, this time for no gain. His total yardage is 2 yards, and the standard deviation for the rounding error is sqrt(.289^2+.289^2) = .408 yards. This is just after 2 runs, and the error is already close to half a yard and growing quickly. Is this going to be a problem as the number of plays adds up? It's still just third down, so Barry has one more play before Detroit has to punt, so let's keep going.

For his third run, Barry rushes for 87 yards. Now his total for 3 plays is 89 yards, with a standard deviation of sqrt(.289^2+.289^2+.289^2) = .500 yards. At one play, the error started out at .289. With the next play, it rose to .408, for an additional .120 yards of error (heh, look at those rounding errors popping up again). On the third run, the error rose by another .092 yards. As you can see, the effect of each additional play diminishes, so maybe it won't be much of a problem after all.

If you read last week's article, you'll remember that the rounding errors were a relatively large issue when PAs were small, but that the error shrunk substantially when PAs became high. The error in yardage we're talking about here won't ever shrink (the only reason the error shrank when we were talking about wOBA was that we divided the error by PA, and the PA term started growing a lot faster than the error term), but its growth will slow down considerably.

Instead of just 3 rushes, let's say Barry Sanders carries the ball 300 times in a season. The standard deviation of the rounding error is sqrt(300*.289^2) = 5 yards. While it only took 3 plays for the error to reach a SD of .5 yards, it took 300 plays to get up to 5 yards. If we look at Barry Sanders whole career of 3000 or so rushes, the SD of the rounding error only gets up to about 16 yards. If some QB goes all Brett Favre on us and keeps slinging the ball every which way until he racks up 6,000 completions (you only need to look at completions for QBs, since the error around the 0 yards for an incompletion can be considered to be 0, spotting errors aside), the SD on the error would still be well under 25 yards. That's pretty reassuring to the way the NFL adds things up.

There still is some imprecision from the rounding, though, so what about the questions introduced in the first paragraph? If a back is credited with 990 yards rushing, what are the chances he was above 1000 without rounding? To answer this, we need to know a little more about the distribution of possible errors. Specifically, we need to know what kind of shape the distribution takes.

If we're lucky, the distribution will be normal, because then the math is simple (or rather, there are plenty of tools readily available that do the math for us, which is a really simple way to "do" math). Remember that we started off with a uniform distribution when we only have one play, which looks like this:



That is clearly not a normal distribution, but the math is still simple with a uniform distribution. When we add a second play, then the distribution for the combined error becomes triangular:



The tip looks a bit rounded in that graph because of how Excel decided to handle things and because I am too lazy to fire up R, but it isn't. It's just straight up triangular. Again, clearly not normal, but still simple enough.

When you add in a third play, then the math for the actual distribution starts to get complicated. It is basically a piecemeal series of polynomial functions that each describe one portion of the distribution. To describe the distribution for combined rounding error for 3 plays, you need 3 different functions, and each play you add means you need to add another function to the mix. What's more, to get the distribution for 3 plays, you need to know the distribution for 2 plays. To derive the distribution for 300 plays, you have to derive the previous 299 distributions as well*, which involves thousands of individual functions. Let's just say, we really don't want to have to use the actual distributions, so we'd better hope that there is a simpler distribution that is a good approximation.

Lo and behold, there is. Here is the actual distribution for 3 plays, along with the normal approximation of the distribution using the same standard deviation:



And here is the actual CDF compared to the normal approximation (click for a larger image, since it is hard to see the difference between the two lines on this graph),



Once you get to 3 plays, the distribution of possible rounding errors starts looking very much like a normal distribution (sweet beans beluga, as my friend would say)**. Which is good, because it's probably what we would have used whether it was a good fit or not, because no one here wants to spend 8 months doing the real math.

Back to the question, how likely is it that the 990 yard rusher got rounded out of his 1000 yard season? It depends on how many carries he had, but if we know he had 300 carries, and we know 1000 yards would require a rounding error of at least 10 yards, then that is a magnitued of 2 SDs for the rounding error. That means there's only about a 2.3% chance that his precise total of 1000+ got rounded down to 990 (rule of thumb is that 95% of a normal distribution is within 2 SDs of the mean, and half of the outcomes outside of the 95% are on the low end of the distribution, leaving about two-and-a-half percent two SD above the mean). If he rushes for 999 yards (poor fellow), then there's a 42% chance he really got rounded down from 1000+. As you can see, it can make a pretty big difference over few yards, but the chances of the error being much more than that diminish pretty quickly. The same thing goes for passing yards for QBs or receivers; most of the rounding errors for a season will be within a few yards. For a 10 year career, the SD for the distribution of errors will be about 3 times as high as for a single year, so adjust accordingly if you want to look at careers (10 yards would be less than 1 SD, so that kind of error would be a lot more common at the career level).

How about if we want to know something like, what are the odds that, if we could have measured their gains with perfect precision, we would discover that Marshall Faulk actually out-gained Jim Brown? According to Pro-Football-Reference, Jim Brown rushed for 12,312 yards in 2359 carries. Marshall Faulk rushed for 12,279 yards in 2836 carries. Those numbers of carries give the distributions of rounding errors SDs of 15.4 and 14.0, respectively. We want to know how much of Faulk's distribution of possible precise totals is greater than some portion of Brown's distribution. To do this, we can subtract their individual distributions. This gives us a new distribution, which is also normal, with a mean equal to the difference between their credited yardages (33 yards) and a SD equal to the square root of the sum of the variances of their individual distributions (20.8 yards). Plug those into your calculator or spreadsheet or table of values of choice to find the odds that x < 0 (meaning the difference Brown minus Faulk is actually negative, which would indicate Faulk gained more yards than Brown), and you end up with 5.6%. Not a lot, but it's there.

Or, how about another close pairing on the all-time rushing list, Corey Dillon and O.J. Simpson? They are just 5 yards apart over a combined 5000 carries. Repeat the math, and there is a 40% chance precise measurements would give Simpson the higher total and that rounding errors pushed him below Dillon.

Really, none of this is very significant. Basically, I just dragged you through 2,000 words to tell you there's not much difference between 11,241 yards and 11,236 yards over a full career. It's common sense, really. Still, I think it's interesting to have an idea of just how little difference it makes that the NFL is so imprecise with its yardage measurements (though, to be clear, this says nothing about the additional errors that would be involved with imprecisions like eyeballing the gain and rounding it incorrectly, or the official mis-spotting the ball, or anything like that; nonetheless, you get the idea that those things are probably not that big a deal here either). At the very least, it's good to know that the rounding errors overwhelmingly fall within the range of what you would look at and say there's really not much difference there.



*I don't know if there is actually a simpler way to derive the actual distributions, just that the only way I do know how to do it is a huge pain in the ass

**Relating to last week's article on rounding errors of wOBA, deriving the actual distributions for the rounding error of wOBA is more complicated since each of the 6 terms in the wOBA formula carries a different weight, so I didn't actually go through with deriving them past 2 terms to compare to a normal distribution. Simulated results for rounding errors of wOBA do appear to fit just as well to a normal distribution, though, so you can probably use the SDs from that article as if they describe a normal distribution as well.***

***If you thought this footnote was going to be about my friend who says "sweet beans beluga", well, that's just because I didn't have a better place to insert a footnote about deriving the distribution for wOBA, so I stuck it in at a largely irrelevant spot. I can tell you, though, that she spells it balooga when she uses it as an interjection, which is kind of interesting (at least compared to footnotes about deriving distributions for possible rounding errors) Continue Reading...

ZiPS ROS Projections as Estimates of True Talent

Player projections are a great tool. They give us good, objective estimates for a player's talent going forward, which makes them useful for addressing a number of questions. For example, should your team go after Player A or Player B to play shortstop next year, and how much improvement does each expect to provide over Player C, who is already signed? How much should the team offer each if they decide to pursue them? Who is a better option to start between two players competing for a job? Did that trade your team just make make sense, and did you get an improvement in expected performance over what you had? How does the talent on my team compare to that of other teams in the division?

You can even use projections for important questions, like, who should I draft first overall in my fantasy league (I guarantee you will avoid such pitfalls as the infamous Beltran-over-Pujols,-et-al debacle, circa 2005--sorry, Uncle Jeff)?

That's all well and good for looking at the coming season, when you don't know anything about anyone's season yet, and your best guess is probably going to be heavily informed by each player's projections. However, the major problem with projections at this point in the season is that most of them are an off-season affair. They are most widely used for projecting the coming season, after all, and they can take a lot of time and computer resources to update to work mid-season and keep running over and over throughout the year.

Each player's current season performance definitely tells us a lot about how we should estimate his talent going forward, so this presents a problem with relying primarily on pre-season projections in some cases. As a result, you are more limited if you want to find projections that incorporate the current season's data to answer questions that require current estimates of talent to answer; say, for example, how does that trade my team just recently made look, or how does my team shape up for the playoffs, and how do they compare to their likely opponents, or should we give a serious look to this September call-up who's been on fire?

Fortunately, there are at least a couple freely available projections that provide in-season updates. The ones I know of are CHONE (published at Baseball Projection) and ZiPS (published at FanGraphs as ROS--rest of season--projections). Both can be good options for estimating a player's current talent level without ignoring information from his performance this season.

Because ZiPS is updated daily (as opposed to the updates every month or so that CHONE provides) and because it is now published at and frequently used by writers for the prominent stat website FanGraphs, it has become a favourite for a lot of fans for estimating current offensive talent for players. While it is great that such a tool is available and that it is used in an attempt to form objective, informed opinions, there is a serious caveat with using the current ZiPS projections on FanGraphs as true talent estimates this late in the season.

To illustrate, consider Ryan Ludwick's ZiPS ROS wOBA projection. Right now, it is .375. Before the season started, ZiPS had Ludwick pegged for a .372 wOBA. He has since aged a bit, posted a .334 figure for the year, and moved to a worse park for hitters. How did his projection go up? What is even more confusing, if you track the projections from day to day, is that yesterday, his wOBA projection was at .390 or so. The day before, it was at .385. And, if you really want to wake up in Wonderland, check the ROS projections during the last week or so of the season, when you have 8 dozen guys projected for the same .250 (or whatever it ends up being) wOBA. What is going on?

The issue is that the ZiPS ROS projections on FanGraphs are not, in fact, an estimate of the player's true talent going forward. Rather, the projection gives its best estimate, in whole numbers, for the player's number of singles, doubles, triples, homers, walks, and HBP for the rest of the season, and then FanGraphs figures out what the player's wOBA would be over the rest of the season if he hit each of those figures on the nose. For Ludwick, that means his .375 wOBA projection is not his projected talent level, but the wOBA for the following projected line:

	1B	2B	3B	HR	BB	HBP	PA	appr. wOBA
Ludwick	8	3	0	3	4	1	55	0.375

But remember that each of those components is rounded to the nearest whole number. His projected singles total could be anything from 7.5-8.5. Rounding to the nearest whole number eliminates precision, and when you have a wOBA figure that needs to go to 3 decimal places, that loss of precision can affect the projected wOBA. To see just how much difference this can make, let's pretend all of Ludwick's actual projected components are really .5 lower than the rounded off whole number (the lowest his actual projected wOBA could be), and then pretend they are all really .5 higher (the highest his actual projected wOBA could be), and see how much that affects his projected wOBA:

	1B	2B	3B	HR	BB	HBP	PA	appr. wOBA
min	7.5	2.5	0	2.5	3.5	0.5	55	0.324
max	8.5	3.5	0.5	3.5	4.5	1.5	55	0.440

As you can see, given Ludwick's projections over 55 PA, his actual projected wOBA could theoretically be anywhere from .324 to .440. That is a huge range. Of course, to be close to the extremes of the range, every component would have to be rounded in the same direction by a large amount, so it is more likely to be close to .375 than to .324 or .440.

How much more likely? To answer that, we have to know something about the distribution of possible true projected wOBAs for Ludwick, given that FanGraphs is displaying a .375 projection over 55 PA. We can do that by finding the standard deviation of the difference between actual projected wOBA and the rounded wOBA projections displayed on FanGraphs for hitters with 55 projected PA.

The actual projected total for each component, before rounding, can be anywhere from .5 less to .5 more than the rounded total. We have no idea where in that range it falls. If Ludwick is projected for 8 singles over 55 PA, it is probably close to equally likely that his true projected rate of singles per PA is 7.5 as it is 8.5, with everything in between being pretty much equally likely. This is a uniform distribution. The standard deviation for this distribution is .5/sqrt(3)=.289. That means the standard deviation for the difference between Ludwick's projected 1B total without rounding and his projected 1B total rounded to the nearest whole number is .289 singles. This describes the error in the rounded total FanGraphs displays.

Since the possible error for every component has the same uniform distribution from -.5 to .5 (except triples, since the rounded estimate of 0 can't have been rounded up, but we'll ignore that for now), the standard deviation for the error of each component is the same .289. Next, we need to know what that means in terms of affecting wOBA. The formula for wOBA is:

(0.72xNIBB + 0.75xHBP + 0.90x1B + 0.92xRBOE + 1.24x2B + 1.56x3B + 1.95xHR) / PA

That means each walk (non-intentional walk, but ZiPS doesn't differentiate, so we'll just use BB) is worth .72 in the numerator of wOBA, each HBP is worth .75, etc. The standard deviation for the error in walk total is .289, so the standard deviation of the effect of that error on the numerator of wOBA is .72*.289 (in other words, the value of each walk times the number of walks). The same process goes for each component. The following table shows the standard deviation and variance for the value of each component to the numerator:

	error	Val	StD Val	Var Val
1B	0.289	0.9	0.260	0.068
2B	0.289	1.24	0.358	0.128
3B	0.289	1.56	0.450	0.203
HR	0.289	1.95	0.563	0.317
BB	0.289	0.72	0.208	0.043
HBP	0.289	0.75	0.217	0.047
combined			0.897	0.805

The combined row shows the total variance and standard deviation for the combined rounding errors. This is simply the sum of the individual variances, with the standard deviation being the square root of that. This is what we are interested in.

.897 is not the standard deviation of wOBA itself, just the numerator. To get the standard deviation for the rounding error of wOBA, we have to divide by the numerator, which, as the above formula shows, is just PA. Ludwick is projected for 55 PA, so divide .897 by 55:

.897/55 = .016

For players projected for 55 remaining PA on FanGraphs, the standard deviation of the difference between their actual projected wOBAs and the rounded off projections which is displayed is .016. If Ludwick's actual projected wOBA is .359, that would put the rounding error in his displayed projection at one standard deviation, which would be a pretty typical observation. Of course, we don't know what anyone's actual projected wOBA is or whether the displayed figure is rounded high or low, just how imprecise the displayed figure is. In some cases, like Ludwick's, we can make a reasonable guess that the projection is rounded in a certain direction based on what we know about how projections work (i.e., a 31-32 year old with a down year probably isn't raising his projection), but all we can do is make reasonable estimates and acknowledge the limitations the imprecision imposes.

What does this mean about the value of ZiPS ROS projections? It depends on how precise you need to be. The precision drops quickly near the end of the year, but earlier in the year, they can work as good estimates of current talent. To determine how much rounding error you can expect in a projection, just divide .897 by the projected PA total for the rest of the season, and that will give you the standard deviation of the error. For example, with 200 PA projected for the ROS, the SD of the error is .897/200=.004, which is a lot more reasonable. At 20 PA, you get .045, at which point you basically can't estimate the difference between anyone with much certainty.

As a result, extrapolating the projections over longer periods of time becomes problematic. For example, if you want to compare players for next season, or to measure the magnitude of the difference between them on a full season scale (i.e., Player A is projected to be worth 30 more runs a year on offense than Player B), you are going to be multiplying the large error in wOBA over a large number of PA. Basically, you can't use them to get an idea of large-scale value.

What they are good for, however, is getting a good guess at expected production over the handful of remaining PAs this season. For example, if you want to scour your fantasy league waiver wire and see what everyone is likely to give you over the rest of the season, or if you want to evaluate a fantasy trade proposal, or whatever, then ZiPS ROS projections are great. The key to using them is, do you need a precise measure of value, and do you need to extrapolate over a large number of PAs? Anything where you are looking for true talent going forward beyond just what to expect over a handful of remaining PAs, or to discuss value in terms of a full season, you'd want to shy away from ZiPS ROS projections more and more the later in the year you get. For applications where you don't care about precision or how the projection extrapolates beyond the remaining 50 or 20 or however many PAs, and you don't need to be able to necessarily pick up differences between players with much certainty, then ZiPS ROS projections are fine.
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Ted Williams, Saberist

One of my first encounters with sabermetrics came when I was a kid visiting Cooperstown for the first time. There, in that house of tradition and history of all places, was an exhibit of baseballs bolted to the wall in the shape of a strike zone, each one painted some colour with some number written on it. The number was a batting average, and the colour corresponded to how hot or cold the average was (from grey or blue for the mid-.200s all the way up to deep red for .400). Together, they replicated a famous chart Ted Williams had put together by keeping track of how well he hit pitches in any location in the zone. Using this information, Williams estimated how well he would be expected to hit on a pitch thrown to any location in the strike zone. You could plainly see his weakness down and away, exacerbated after he shattered his elbow in 1950, as well as how quickly that target zone for pitches turned into a .380+ wheelhouse for Williams if the pitcher missed by the slightest of margins.

I later learned that the chart the exhibit was based on was from a book Williams had written called The Science of Hitting (I probably learned it reading the info from the exhibit, actually, but I relearned and remembered it later). The book embraced the objective, analytical thought processes that form the basis of sabermetrics. Nowadays, analysts like Jeremy Greenhouse are still building on the work Ted Williams was doing already decades ago. So when I was browsing through interviews of the Splendid Splinter recently, it's really no surprise to find him espousing sabermetric wisdom right and left.

Tangotiger of The Book Blog recently praised contemporary sabermetric spokesman Brian Bannister for speaking intelligently on the role of luck in the game. While a player's skill is clearly a huge part of his success or failure in the game, it's also impossible to ignore that random chance also factors into that success, and sometimes the effects of that random chance can play a significant role. As one of the most sabermetrically-minded players in the game today, Bannister understands this.

So too, it seems, did Ted Williams. From a Sports Illustrated interview:

TW: I've been a very lucky guy. Even I know how lucky I've been, especially in my baseball career. Anybody who thinks he's had great success or outstanding success, he's a lucky guy. You're damn right.

One of the key ideas to understanding future performance in baseball is that if someone has performed at a spectacular level, and you want to estimate how he will perform in the future, chances are he will not perform as highly as he did before. That concept is usually called regression to the mean. Basically, if you have a hitter who just hit for a .400 wOBA, it is possible that his actual expected level of performance is .400, and he hit just like he was expected too, and it's possible that he is really an expected .420 hitter who got unlucky and only hit .400, but it's more likely that he's really an expected .380 hitter who got a bit lucky to hit .400. Another way to look at it is if you take all the hitters who hit for a .400 wOBA over a given period of time, and you look at what they do after that. A few of them will keep hitting at .400 or better, but most of them will regress, and as a group, they are very likely to hit at a lower level going forward. If you take any one hitter from that group and try to predict whether he will be one of the few who improves or one of the many who declines, the odds are greater that he will decline.

That's why whenever we have a player who has performed at a high level, we know, with some degree of certainty, that he was a really good player, but we also know that there was a better than average chance that he was a bit lucky too, and that he really hit a bit higher than his expected level of performance. Ted Williams sums this up rather succinctly in the above quotation.

He expands on this in another interview, this time with Esquire:

Somebody will hit .400, maybe .410 or .415. Oh, you bet. It’s a hard thing to do. Ya gotta be lucky. Baseball might be a little tougher today. They bring in a new pitcher any old time. Ya gotta go through that whole ritual again of trying to find out as much as ya can on six pitches. Ya hit at him four times, ya got a chance of gettin’ him locked in a little better.

In addition to the special bonus material discussing the benefit of facing a hitter multiple times through the order and the added difficulty of hitting relievers even though they are worse pitchers talent-wise than starters (these are among the subjects explored in the modern sabermetric masterpiece The Book), we see Williams again discussing the role of luck, this time in perhaps his most historic feat as the last hitter to bat .400. Williams again says it simply, "Ya gotta be lucky." When he says someone will hit for a .400 AVG again, he acknowledges the role of chance in the game, that there is random variation in a hitter's performance, and that while no one is truly an expected .400 AVG hitter, sometimes, by chance, hitters hit several points above their actual talent level, and that if you have enough true .330 hitters play enough seasons, eventually one of them will get lucky and hit .400. You've got to be good to even have a chance, but you've also got to be lucky to be the one it happens to.

Going back to the Hall of Fame exhibit, Williams marked his hottest zone right at the center of the strike zone, 1 ball wide and 3 balls high. In that area, just 3 balls across, Williams estimated that he was a true .400 hitter. That was as good as he got, and nowhere else in the zone was even Ted Williams that good. When he hit .400, it was basically like he hit the whole season as if pitchers were grooving every pitch right down the middle to him. Obviously, they weren't putting every pitch right down the middle, so it's easy to see from the chart how it is impossible that Williams could have hit .400 simply because that was his true talent level, and not because he was a ridiculously good hitter who also benefited from some good luck that season and hit better than his expected level. He had to have gotten lucky, and Williams openly acknowledges this.

Earlier in the Esquire interview, Williams discusses another way in which he was lucky:

I was lucky. I’m talkin’ about the fifty thousand balls that was thrown at me, the times I slid, the times I fell. You gotta be lucky to have longevity in any sport. It’s a tough routine. Some people are just a little inherently more tough than the next guy. I think that’s God-given genetics.

Williams had a truly great career as one of the best hitters the game has ever seen. He hit like no one else in the game, and he kept doing it from the time he broke into the Majors at age 20 right up into his 40s. It takes incredible talent and devotion to do that, but, as Williams plainly points out, it takes a lot of luck too. Even with all the talent and desire and work ethic in the world, you can still end up like Herb Score or Dick Allen or Andre Dawson or Jim Edmonds or Ralph Kiner, players whose luck broke the wrong way, to varying degrees, at one time or another and left them never quite the same. Bad luck can push you right out of the game; it can leave you a semi-productive shell hanging around for years; it can even leave you a still-immense talent, robbing you only of the chance to be one of the small handful to reach the Ruthian peak of the game's history. However it gets you, it can get anyone at any time, and for all the players who may have had the talent and everything else to reach that peak, very few have the luck Williams did to be allowed to actually reach it.

None of this is intended to take anything away from how great Ted Williams really was. Williams was an honest and candid man who had no trouble placing himself among the handful of greatest hitters of all-time, and he was absolutely right to place himself there (and this isn't to say he was arrogant about it either; he also refused to claim to be the greatest hitter ever or that he had distinguished himself from the other handful of greatest hitters even as many felt he was and had). The same honesty let him publish his chart saying that he was only truly a .400 hitter on the very fattest of pitches, and saying that if the pitcher could paint the lower-outside corner perfectly against him, he could be reduced to a .230 hitter. Part of that honesty is that Williams had the sense to understand that no matter how great he was, his greatness was enabled by good fortune along the way, and that no amount of greatness can erase the role of chance and luck in the game. It's that honest pursuit of objective knowledge of the game that makes Ted Williams a perfect pioneer in the field of sabermetrics. He looked for the truth of the game around him and learned to understand its workings, and then he very matter-of-factly presented the truths he learned with no bias toward his own career or his teammates or anything other than what he saw to be true. And that, in essence, is sabermetrics.
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Hammering Away at the Derby Effect

The HR Derby is having trouble finding participants these days. Players and teams alike are removing themselves or their employees from consideration for fear of hurting their swings and/or their bodies. It's not too hard to cite a handful of players who did well in the Derby only to launch into a second-half slump (exhibit A: Josh Hamilton hits 56* first half home runs in 2008, only to drag across the finish line with a paltry 11 second-half dingers), so it's unsurprising that players and teams spare few precautions for something widely deemed meaningless.


Players who have been confirmed or rumoured to have turned down invitations to participate this year include:

Albert Pujols
Justin Morneau
Ryan Howard
Torii Hunter
Robinson Cano
Ichiro
Micah Owings
Mark McGwire
Barry Bonds
Bobby Thomson (initially agreed, but declined upon learning Ralph Branca would not be available to pitch his rounds)

Meanwhile, eventual participants Chris Young, Corey Hart, Nick Swisher, Vernon Wells, and Hanley Ramirez entered 2010 with collectively fewer home runs than Alex Rodriguez despite their 5500 PA head start.

This article isn't about whether or not the HR Derby sucks, however. After all, it could be worse. For example, it could be the Texas League HR Derby, which was thrown into controversy when participant Koby Clemens failed to homer once after reportedly being threatened with a beaning if he dared take his dad yard. Rather, this article is about whether there has really been any detectable Derby hangover effect holding hitters back.

Before I begin, I should mention that Derek Carty looked at this very issue last year at THT. He has also recently published a follow-up on the same site using a different method. The approach I take here is more similar to the second Carty approach (compare Derby participants to a control group) than to the first (compare Derby participants to their own pre-season projections), but it is worth noting that Carty found similar results using both methods.

Whereas Carty focused only on AB/HR in each half of the season, I have chosen to instead look at all around hitting using wOBA and total PAs. My reasons for doing so are twofold:

-Derek Carty has already demonstrated, with mostly the same data I am using, that the Derby hangover has not manifested itself in worse HR frequencies than expected
-It is possible that if players or teams are concerned about the Derby affecting a player's swing, that could result in the player hitting just as many HR but suffering in other areas of hitting, which would reflect in wOBA but not AB/HR

For my study, I looked at the 80 participants in the HR Derby from 2000-2009 (some of those 80 participants are really just different seasons for the same player). For each player, I split his season into pre- and post-All-Star-Break and recorded his wOBA (by the way, the wOBA I am using here does not include SB/CS, only batter events) for each half. Here are the results:

1st Half			2nd Half			Diff
wOBA	PA	|	wOBA	PA	|	wOBA	PA
0.411	29621	|	0.401	23182	|	0.010	6439

Like Carty, I found a drop in performance from 1st half to second half for Derby participants, but not a very large one, and, as Carty points out in his work, we would expect to see a drop in performance from any group of players who performed that well in the first half. As for how much of a drop we should expect, or whether a drop of .010 points in wOBA is indicative of a hangover effect, well, that's what we need to look at our control group for.

Carty manually selected comps for each Derby participant for his control group in his second study. Rather than repeat his process, I used a simple rule to select my control group. I just ranked all non-Derby participants in each season by first-half wRAA and took the top 8 from each season. I sorted by wRAA rather than wOBA to make sure I was not taking players with a great wOBA in small number of PAs (since they would not make good comps and would be expected to have significantly more regression in the second half than the Derby-participant group). I could have also set a minimum PA threshold and sorted by wOBA; either way accomplishes more-or-less the same thing.

Ideally, we want our control group to be as close as possible to the Derby-participant group in the first half so that we can make a good comparison of their second half performances. Let's see how the two groups compare:

	1st Half			2nd Half			Diff
	wOBA	PA	|	wOBA	PA	|	wOBA	PA
Derby	0.411	29621	|	0.401	23182	|	0.010	6439
Control	0.432	28774	|	0.400	21442	|	0.032	7332

Here, we see that our control group lost .032 points in wOBA, way more than the Derby participants lost. What's more, the control group lost more PAs in the second half, so not only are the Derby participants holding up their rate production better; they're also staying in the lineup more, which is important because of the commonly cited health concerns over Derby participation.

Before we get too excited over these results, we should consider that they could simply reflect an issue with the control group. After all, it doesn't really make sense that over 20-30 thousand PA samples, the control group should lose an extra .022 points in wOBA and about 12 extra PAs per hitter in the second half over the Derby participants. If our control group were properly selected and actually representative, this would suggest that the Derby could actually be helping hitters significantly in the second half, and there's no reason to believe that to be the case. So before we accept these results, let's consider what issues might exist with the control group.

The first thing to notice is that we wanted both groups to come out as close as possible in the first half. However, the control group had a significantly higher first-half wOBA, as well as fewer first half PAs. This is by itself problematic. Remember that we expect any group that performs exceptionally in the first half to regress in the second half. The more exceptional the performance of the group in the first half, the more we'd expect them to regress in the second half. Additionally, the fewer PAs each player in the group has taken, the more we'd expect them to regress. An extra .021 points in first half wOBA and fewer PAs per player in the control group mean we would expect more regression in the second half than for the Derby group (which, in short, means this is not a good control group).

One possible way to address this is to select more players for the control group. If the top 8 non-Derby participants each year are collectively much better in the first half than the 8 Derby participants, we can select more hitters until the control group hits at about the same level as the Derby group. For example, while the top 8 hitters in the non-Derby group each year have hit at a .432 level, the top 20 might hit at close to a .411 level, which would make for a better control group. Unfortunately, that would still leave a likely problem.

As noted, even though the control group hit significantly better in the first half than the Derby group, they had fewer PAs, which is a bit unusual since we selected the control group based on the top performing hitters (who are generally given a lot of PAs). A possible reason for this presents an even bigger problem for our control group. With our Derby hitters, we know that they performed well in the first half, and that they were healthy at the All Star Break (at least healthy enough to participate). With our control group, we know that they performed well in the first half, but not that they were healthy at the All Star Break. We also know that, despite out-hitting the Derby participants as a group in the first half, they didn't participate in the Derby. There are many reasons hitters sit out the Derby, including pulling themselves out or being passed over for more well-known if less stellar-performing hitters, but one potential reason for a top-performing hitter to not be in the Derby is that he can't because he is already hurt. This was likely the case for a small number of hitters in the control group. It explains why the control group had fewer PAs despite performing better, as well as why they lost more PAs in the second half and why they regressed so much more.

Since we know none of the hitters in the Derby group were hurt as of the All Star Break, having any injured players (as of the All Star Break) in the control group will screw up the control group. Players who got hurt in the first half and still showed up in the top 8 in wRAA would have to have had a really good wOBA in the first half. That means when we look at the second half for their group, the group not only loses PAs because a player is already hurt going into the second half, they also lose a high-wOBA player, so even if everyone else regresses normally, the group as a whole will regress more than expected from losing one of its better hitters.

What this means for our control group is that we need to ensure that we have the same restrictions on our control group as we have on our Derby group. Namely, we need to ensure they were healthy going into the All Star Break. This is simple enough to do; we can do it the exact same way we got that restriction on the Derby group in the first place. We'll simply select only from the pool of players who participated in the All Star Game but not in the Derby.

More specifically, I narrowed the pool of players for the control group to the ASG starters (just because that was the simplest way to ensure an All Star actually participated and was not just selected, and because, as we'll see, doing so gave me a pretty good match for the control group) who did not participate in the HR Derby. I took the top 8 such players from each group each year (again, sorted by first half wRAA). Now, here is what the new control group looks like compared to the Derby group:

	1st Half			2nd Half			Diff
	wOBA	PA	|	wOBA	PA	|	wOBA	PA
Derby	0.411	29621	|	0.401	23182	|	0.010	6439
Control	0.411	28768	l	0.395	22332	|	0.016	6436

Still fewer PAs, but an exact match on the wOBA, and we eliminated the issue of having already-injured players in the control group that was throwing off the comparison before.

With this group, we see that the loss in both wOBA and in PAs is pretty close for both groups. It's possible there are still some selection issues with the control group; for example, Derby participants over the last 10 years might tend to be more highly regarded hitters, so, even though they performed at the same level, they were given more PAs and had a slightly higher true talent level, which would cause them to regress a bit less. Still, I think the restrictions placed should take care of the most important problems, and this control group should be good enough to pick up an effect if there were one. The extra .006 points in wOBA the Derby group held over the control group seems like a pretty reasonable cushion to absorb any further problems with the control group. That gap could be explained by remaining minor selection issues, but with the injury problem controlled for, I think the control group is good enough to demonstrate a lack of any easily detectable effect.

Based on this control group, we see that not only have Derby participants not lost any home run frequency in the second half over what was expected, as Carty has shown; they also have not lost any overall hitting performance or total PAs. What that tells us is that, compared to other All Star participants who had equally good first halves at the plate, hitters who participate in the Derby haven't lost any more production or playing time over the past ten years. If there is any meaningful hangover effect, it is certainly far more subtle and less nefarious than it is often made out to be, and it's not reflected in a simple glance at the second half numbers.


*56 first half HR includes 35 unofficial HR from Derby itself Continue Reading...