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Best Sluggers of All Time

by Alfredo Nasiff Fors

The barrage of Home runs this MLB season resulted in a stratospheric number of broken records. Each year has its own peculiarities, though not as remarkable as this 2019 when even the MLB Commissioner’s Office was prompted to admit that some changes were made to the ball. If we look at the HR/AB mean per season, we would see the differences from one year to another that, independently of the many causes, must be taken into consideration by statisticians when drawing comparisons among sluggers from different decades.

Source: Lahman database in R (for all graphs)

The trend indicates that nowadays, it is easier to hit Home runs than at the beginning of MLB history. We can speculate that if Babe Ruth would have played in modern times, the total of his Home runs would have been higher, but we certainly will never know that. Nevertheless, comparisons can be made among players from different times, weighing in their performances against the rest of the players of each Season played, meaning dividing the individual performance against the Season mean of the parameter we are studying.

The purpose of this paper is to make the comparison of the greatest Home run sluggers of all times against the mean of each season played by them.

We begin by presenting the case of Babe Ruth as an example. The following graph shows HR/AB by year with three lines drawn, one represents Babe Ruth’s index, another one represents the mean of the Season and the last one the Rate, which is [Babe Ruth (HR/AB)]/ [Season (HR/AB)]. In 1920 Babe Ruth HR/AB index was 0.118; the Season mean was 0.007; therefore, he had a frequency of HR/AB 15.8 times above average (the Rate).

Adding up the Rate index accumulated by Babe Ruth along his entire career will make a total of 157.2 times above Seasons mean:

How does this compare with the other players listed among the top HR sluggers, the following table shows it in descending order by number of HR:

  Name Years AB HR (HR/AB) / Season(HR/AB)
1 Barry Bonds 1986-2007 9847 762 61.2
2 Hank Aaron 1954-1976 12364 755 59.3
3 Babe Ruth 1914-1935 8398 714 157.2
4 Alex Rodriguez 1994-2016 10566 696 43
5 Willie Mays 1951-1973 10881 660 51.7
6 Albert Pujols 2001-2018 10196 633 36.6
7 Ken Griffey 1989-2010 9801 630 46.7
8 Jim Thome 1991-2012 8422 612 51.5
9 Sammy Sosa 1989-2007 8813 609 39.4
10 Frank Robinson 1956-1976 10006 586 53.9
11 Mark McGwire 1986-2001 6187 583 57.4
12 Harmon Killebrew 1954-1975 8147 573 54.5
13 Rafael Palmeiro 1986-2005 10472 569 36.8
14 Reggie Jackson 1967-1987 9864 563 51.3
15 Manny Ramirez 1993-2011 8244 555 41.2
16 Mike Schmidt 1972-1989 8352 548 50.2
17 David Ortiz 1997-2016 8640 541 37.4
18 Mickey Mantle 1951-1968 8102 536 47.8
19 Jimmie Foxx 1925-1945 8134 534 71.5
21 Frank Thomas 1990-2008 8199 521 43.1
22 Ted Williams 1939-1960 7706 521 67.4
20 Willie McCovey 1959-1980 8197 521 56.9
24 Eddie Mathews 1952-1968 8537 512 43.4
23 Ernie Banks 1953-1971 9421 512 40.7
25 Mel Ott 1926-1947 9456 511 70.4
26 Gary Sheffield 1988-2009 9217 509 42.8
27 Eddie Murray 1977-1997 11336 504 39.4
29 Fred McGriff 1986-2004 8757 493 41
28 Lou Gehrig 1923-1939 8001 493 63.7
30 Adrian Beltre 1998-2018 11068 477 29.1
31 Stan Musial 1941-1963 10972 475 43.2
32 Willie Stargell 1962-1982 7927 475 50.5
33 Carlos Delgado 1993-2009 7283 473 31.9
34 Chipper Jones 1993-2012 8984 468 30
36 Dave Winfield 1973-1995 11003 465 39.5
35 Miguel Cabrera 2003-2018 8456 465 28
38 Adam Dunn 2001-2014 6883 462 34.7
37 Jose Canseco 1985-2001 7057 462 44.5
39 Carl Yastrzemski 1961-1983 11988 452 38
40 Jeff Bagwell 1991-2005 7797 449 28.1
41 Vladimir Guerrero 1996-2011 8155 449 27.4
42 Dave Kingman 1971-1986 6677 442 58.6
up to 2018

At first glance, one thought jumps out from the table: Babe Ruth “(HR/AB) / Season (HR/AB)” more than double the next player in the list.

If we look carefully, will see that in recent years it is tougher for players to excel above the mean, note that the only two active players on the list are doing very badly in the Index, which can be explained by the rise in the mean as seen in the first chart, or in simple words, it is harder to be the leader when everybody else hit a lot of Homeruns. This trend has multifactorial causes, among them it will be explored the hypothesis that these days players hit more Home runs thanks to the rise in competitiveness due to the fact that the selection process is made from a larger number of players, Teams, Leagues, training camps, and international contracts, and the advancements made in the technology applied to enhance performance, which plays a major role in nutrition, fitness, statistics, etc.

Could factors such as competitiveness and enhanced performance be measured? The proposition is to use the weight and height of the players as an expression of how those factors have improved their physical traits and therefore quantify how has this affected the mean of HR per Season.

The “Strength” of players will be then, the addition of both their height (in inches) and weight (in lbs.), reasoning that the taller and corpulent the player the farther will go his connections. Plotting the mean of each season, the graph looks like this:

It is effectively seen that in recent times, the players are stronger, therefore making it harder for power hitters to excel above the mean. In 1920 the Strength mean was 243.2 while Babe’s Strength was 289, taking over 45 points of advantage. In 2011, the year Mike Trout debuted with a Strength of 309, the mean topped the all-time list with 285, a meager 24 points below.

Recalculating the “Times_HRperAB_over_SeasonMean” Rate dividing it by the “Times_Strength_over_SeasonMean” resulting in “Times HRRate_vs_StrengthRate”, shows the difference in “Diff_HRperAB_vs_Strength”:

  Name Years AB HR Strength Times_HRperAB_

over_SeasonMean

Times_Strength_over

_SeasonMean

Times_HRRate_

vs_StrengthRate

Diff_HRperAB

_vs_Strength

1 Barry Bonds 1986-2007 9847 762 258 61.2 21.4 63.3 2.1
2 Hank Aaron 1954-1976 12364 755 252 59.3 22.5 60.7 1.4
3 Babe Ruth 1914-1935 8398 714 289 157.2 25.9 133.2 -24
4 Alex Rodriguez 1994-2016 10566 696 305 43 24.4 38.8 -4.2
5 Willie Mays 1951-1973 10881 660 240 51.7 21.4 55.5 3.8
6 Albert Pujols 2001-2018 10196 633 315 36.6 20.3 32.5 -4.1
7 Ken Griffey 1989-2010 9801 630 270 46.7 23 46.4 -0.3
8 Jim Thome 1991-2012 8422 612 326 51.5 29.9 43.2 -8.3
9 Sammy Sosa 1989-2007 8813 609 237 39.4 16.9 44.4 5
10 Frank Robinson 1956-1976 10006 586 256 53.9 21.9 54.2 0.3
11 Mark McGwire 1986-2001 6187 583 292 57.4 18.9 51.7 -5.7
12 Harmon Killebrew 1954-1975 8147 573 267 54.5 22.8 52.6 -1.9
13 Rafael Palmeiro 1986-2005 10472 569 252 36.8 19.1 38.7 1.9
14 Reggie Jackson 1967-1987 9864 563 267 51.3 21.8 49.5 -1.8
15 Manny Ramirez 1993-2011 8244 555 297 41.2 22.9 37.7 -3.5
16 Mike Schmidt 1972-1989 8352 548 269 50.2 18.8 48.1 -2.1
17 David Ortiz 1997-2016 8640 541 305 37.4 22 34.2 -3.2
18 Mickey Mantle 1951-1968 8102 536 266 47.8 18.6 46.3 -1.5
19 Jimmie Foxx 1925-1945 8134 534 267 71.5 22.4 67.1 -4.4
20 Willie McCovey 1959-1980 8197 521 274 56.9 24.4 53.6 -3.3
21 Frank Thomas 1990-2008 8199 521 317 43.1 23.6 36.5 -6.6
22 Ted Williams 1939-1960 7706 521 280 67.4 20.8 61.6 -5.8
23 Ernie Banks 1953-1971 9421 512 253 40.7 18.6 41.4 0.7
24 Eddie Mathews 1952-1968 8537 512 263 43.4 18.4 42.5 -0.9
25 Mel Ott 1926-1947 9456 511 239 70.4 21 73.9 3.5
26 Gary Sheffield 1988-2009 9217 509 261 42.8 23.4 43.8 1
27 Eddie Murray 1977-1997 11336 504 264 39.4 23.4 38.6 -0.8
28 Lou Gehrig 1923-1939 8001 493 272 63.7 18.6 58.1 -5.6
29 Fred McGriff 1986-2004 8757 493 275 41 21.9 39.2 -1.8
30 Adrian Beltre 1998-2018 11068 477 291 29.1 22 27.9 -1.2
31 Stan Musial 1941-1963 10972 475 247 43.2 21.2 44.8 1.6
32 Willie Stargell 1962-1982 7927 475 262 50.5 21.4 49.7 -0.8
33 Carlos Delgado 1993-2009 7283 473 290 31.9 18.2 29.8 -2.1
34 Chipper Jones 1993-2012 8984 468 286 30 19.9 28.6 -1.4
35 Miguel Cabrera 2003-2018 8456 465 325 28 18.5 24.3 -3.7
36 Dave Winfield 1973-1995 11003 465 298 39.5 26.5 34.3 -5.2
37 Jose Canseco 1985-2001 7057 462 316 44.5 22.9 36.9 -7.6
38 Adam Dunn 2001-2014 6883 462 363 34.7 20.8 26.8 -7.9
39 Carl Yastrzemski 1961-1983 11988 452 246 38 22 39.8 1.8
40 Jeff Bagwell 1991-2005 7797 449 267 28.1 15 28 -0.1
41 Vladimir Guerrero 1996-2011 8155 449 310 27.4 18.1 24.1 -3.3
42 Dave Kingman 1971-1986 6677 442 288 58.6 21.3 52.4 -6.2

The largest differences were accounted by Babe Ruth (-24) who still almost double his closest tracker (Mel Ott, who displaced Jimmie Foxx of the second place thanks to his low 239 Strength) and Sammy Sosa (+5). So, the physical traits of Babe Ruth (74 in + 215 lbs = 289) impacted negatively in his “Times HR/AB over Season mean”, as the other players of his time were in physical disadvantage with him, looking like kids playing around with a Pro.

Corollary

Babe Ruth, despite this later skirmish using the Strength statistic, seems to be once again, immovable as the Greatest Player of All-Time.

Big names show up topping the list of the “Times HRRate_vs_StrengthRate”: 1-Babe Ruth; 2-Mel Ott; 3-Jimmie Foxx; 4-Barry Bonds; 5-Ted Williams; 6-Hank Aaron; 7-Lou Gehrig; 8-Willie Mays; 9-Frank Robinson; 10-Willie McCovey; 11-Harmon Killebrew. Make your own judgment.

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Proven Closers: Further Debunking of a Worn-out Narrative

 

Throughout the MLB’s long and storied history, narratives have defined fans’ enjoyment of the game as well as many teams’ front office strategies and teambuilding. From the fan perspective, these stories build the mythology and lore of the sport. But from the team perspective, these unbacked assumptions can often be harmful and limiting. One of the best examples of this is the long-held belief that only a special, “mentally tough” reliever is capable of handing the stress of finishing a game. These “proven closers” supposedly have the guts and experience to hold up under the immense weight of securing a save. This assumption has finally started to be questioned  in recent years as advanced analytical front offices have conducted rigorous testing on many of these ancient baseball narratives. This can be easily seen by doing a simple glance at who successful current teams trust with the role compared to ten years in the past.

For this study, I used players who had at least 75 saves in the years preceding the chosen year as my proven cutoff. These pitchers would have recorded enough experience to allegedly calm their nerves and earn the title of proven closer in the public baseball lexicon. During the 2010 season, 60 percent of the league’s top 15 teams had these guys finishing games. That’s lower than the previous decades, but is huge compared to this season: In 2019 only 40 percent of the top 15 teams fielded these wily, validated veterans. While some are top pitchers in the league, including the likes Aroldis Chapman, Craig Kimbrell, and Sean Doolittle, the other group is just as effective. Inexperienced guys like Luke Jackson, Josh Hader, and Taylor Rodgers have taken the league by storm, and while they often are not as publicly recognized, they are often just as effective.

This, however, is all just anecdotal evidence. For the rest of this paper, I will evaluate whether this recent strategical change is valid and if being a proven closer really does make you more qualified to man the most continuously stressful position in the game.

The first test I will be running is a general comparison between the “proven closer” and the not-proven (which I’ll call the “young’uns”) for 2010 and 2019. One interesting part of this comparison is that these relievers over their careers have each played both roles at certain points. Due to this, you obviously can’t just take career stats for guys with 75 saves and those without. This can be easily solved by taking each season as its own individual data point. This will cause players like Mariano Rivera to have around 15 data points in the proven closer bucket, meaning he will have a larger impact on the end results than, for example, Luke Jackson. But this is fair, as his sample is much larger and deserves to have higher weight than a guy who has pitched only 30 innings in save situations.

After I built the aforementioned datasets, I took weighted averages of the two groups’ performance in save situations. The statistics taken into account were ERA, WHIP, K/9, and OPS allowed. I would have preferred to use some different metrics, but the ease of Baseball Reference’s save situations splits led me to use their numbers, which should be more than fine for this exercise. I then took the means of each of the previous statistics for both player buckets. This was used to run Welch’s T tests, a statistical method which tests differences in datasets that have different sample sizes. The results for every stat were pretty comparable across the board and actually gave some good insights. I did expect the proven closers’ numbers to look relatively similar to the “young’uns” numbers, but with maybe slightly better results due to the higher weight on a few very good players, like Rivera and Hoffman. But what I found was the young’un group significantly outperformed their proven counterparts on all stats across the board. ERA, which is probably the most important stat I tested, had the young’un group coming in at a 2.95 ERA in save situations compared to a 3.09 ERA for the vets. This may not seem like a lot, but when this data is tested, that result ended with a .21 p value when the null hypothesis used was the proven group having a lower ERA. In more layman’s terms, this means that in this dataset, it is very unlikely that this difference was just random variation and that having 75 career saves does not lead to a lower ERA in save situations. This discovery was consistent among the other statistics. While this provides some evidence to prove the irrelevance of the proven closer motif, these results could have resulted from other biases in the dataset, including that the young’uns are, well, younger, and that most guys who recently entered the closer spot are playing at the top of their game. This makes it necessary to conduct further testing if we want to say more confidently that the “proven closer” is myth.

One of the main issues with doing overarching quantitative analysis on this subject is the biases created by the uneven opportunities given by teams. In other words, playing time is not randomly generated for the players in the league. Teams are trying to win, and it obviously hurts this goal to have subpar pitchers on the hill during the highest leverage part of the game. To account for this, we need to create a baseline talent for each player. That way we would isolate pitching in the 9th inning as the only variable. To do this, I compiled the statistics for each player in the previous datasets for both save and not save situations. If having 9th inning experience makes an actual difference, the proven closers bucket would show a much larger negative delta when compared to the young’uns.

 

The results were as follows:

ERA Save SIt ERA Non-Save Sit OPS Save SIt OPS Non-Save Sit
Proven Closer 3.11 3.39 .636 .663
Young’uns 3.65 3.75 .683 .705

 

As you can see the differences, while pretty similar, are larger for the proven closer group. This slightly points towards proven closers having an edge. But this is not as significant a difference as the previous study when taking into account the smaller sample size from not repeated players. This makes an interesting counterargument to the previous point and definitely requires further testing.

The last statistical method I used incorporated predictive modeling into the equation. This could potentially add an extra layer of noise to the study, but I believe it’s worth it if you take that into account.  My idea was to create a usable model that predicts a closer’s save situation ERA from a variety of different inputs, but excluding everything that has to do with experience in the role. A few examples are innings pitched, saves, and any counting stat. From this starting point, I was able to build an OK but admittedly limited multiple linear regression model with various rate based inputs ranging from FIP, to K%, to HR/9. My final model ended with a mean absolute error of .4 on the validation datasets, which means it on average missed its target by a distance of .4 ERA.

After this, I input my two proven and unproven data buckets and evaluated the error metrics for each. What I was looking for was the model to predict the proven players bucket significantly worse than they actually were, and the unproven players significantly better. This would show that there was some hidden skill not accounted for in the model on top of just random noise. While we can’t say for sure what that skill would be, I attempted to design a model that would make that the most likely missing piece.

The results were the exact opposite. The model predicted the unproven guys to be worse than they actually were while the proven guys to be better. This easily could have been just variation, so I decided to run another Welch’s T Test to evaluate how significant the difference in error was. It was very confident that the mean error did not predict better results than actually seen for the proven guys and worse results than actually seen for the unproven guys. This was evident by a .9997 p value, which is a very significant number. Of course, this has to be taken with a grain of salt due to the previously mentioned issue of the added noise from my not-perfect predictive model. Nevertheless, this level of certainty is good evidence and backs the idea that experience in the 9th isn’t an important factor.

In conclusion, my quantitative studies, for the most part, back the industrywide trend of no longer relying on the archetypal “proven closer”. The abundance of 9th inning experience does not seem to make any significant, quantifiable difference. While the second test conducted didn’t back this claim, the other two studies more than did. This makes sense. A good pitcher is a good pitcher. And while it is difficult to deal with stress when you haven’t before, these are professional athletes who play on the biggest stage in the world. They deal with immense stress every day. In order to get to this level of excellence in this failure-based game, they have to be mentally tough. No matter how many career saves you have, pitching the 9th inning of a close game with playoff applications is just another day at the office.

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Old vs New and the Pitcher Development Battle

In baseball today, the importance of player development is no longer debatable. Everyone has rallied around the necessity of impactful training. Instead, battles are now erupting over which skills are crucial to develop in young players.

Pitchers are the focal point of these debates as new-school biometric training facilities have revolutionized the process by concentrating on improving spin, mechanics, and velocity. This strategy has been met with harsh counter claims from older-school coaches who emphasize repeatable mechanics, control, and pitch sequencing over the “glamour” skills that the newer biometric companies covet.

This battle has been waged in MLB front offices for years, and at this point, the new training seems to be winning. This past off-season, biometric data-driven coaches were brought in by numerous franchises. These guys are touted as the future of player development. But are the new skills that they focus on any more strongly positively correlated with MLB success than the older school skills? In this analysis, we will dive into the data and find out which underlining skills are the most crucial to big league success and whether these new school guys are in fact correct.

As you can probably guess, each individual skill will have a very low correlation to overall success. That’s because pitching is a complex conglomerate of skills that can be combined in myriad ways and still be very successful. There is no perfect mix. With that being said, when you compare each skill, you can still see which ones as a whole contribute most to the overall package and are, in general, the most crucial. The skills I chose to test are as follows, generically grouped into old school and new school:

Old School

Ability to locate pitches: Measured by BP’s CMD Metric

Ability to change speeds: Measured by velocity drop off between primary fastball and off-speed

A mixed arsenal: Measured by breaking ball, off-speed, and fastball percentages of pitches thrown.

New School

Fastball Velocity: Measured by highest average fastball velocity

Fastball Spin Rate: Measured by average RPM for 4 primary fastball

Slider Spin Rate: Measured by average Rpm for all pitchers who threw the offering at least 5% of the time

Curveball Spin Rate: Measured by average Rpm for all pitchers who threw the offering at least 5% of the time

The first step was isolating each variable’s effect on success. I quantified success as MLB ERA instead of more pitcher isolation based metrics like Fip. I did this for two reasons. The first is that Fip and its follower stats tend to be biased toward pitchers who lean towards the more new-school approach. This is due to the old -school approach’s emphasis on generating weak contact, which FIP factors out completely. Using ERA puts both skillsets on relatively the same playing field, even though it may give pitchers too much credit for weak contact in turn, slightly helping out the old school. Second, I wanted to measure the most basic definition of pitcher success: limiting runs. This keeps it simple and is easily understood by the general public.

After running simple linear regressions for each of my 9 variables (I split arsenal into 3 parts for the regressions) it became clear that some of the variable had zero or almost zero impact on ERA when isolated. These included some that I assumed beforehand — like breaking ball percentage thrown and changeup percentage thrown — but also more interesting discoveries, including command and ability to change speeds. Here are the plots:

 

Both of these highly touted old-school skills failed the correlation test, having a basically 0 correlation coefficients and flat slopes, meaning as they get better ERA doesn’t follow suit. This might have to do partially with sampling bias as only data from the last two season of Statcast is publicly available. This limits my sample to the current game, which has trended away from maximizing these skills. Even so, this level of separation from ERA is very noteworthy and should be taken into account. The data is not saying these skills are not entirely unimportant, as they are good auxiliary skills, but just that they alone aren’t enough to drive success.

Next, I will delve into what skills my analysis found most predictive of MLB success. These were, as new school advocates already are aware of, fastball velocity and fastball spin rate, followed by slider spin.

Each of these skills still may seem to have a small R^2 at .08, .078, and .023 respectively but when isolating a single trait, the first two are about as good as you can ask for. Just like you wouldn’t have a shot at telling me a player’s ERA if you just knew he threw 93 MPH, the computer can’t really tell, either. But the computer does have a better shot at it with those two skills than any other I measured, by a wide margin. Another important finding about these three skills is that they each have relatively steep negative slopes meaning as they increase, ERA will fall with them. Velocity and spin rate have been buzz words in baseball for years now and this is just more backing for them to gain further influence in the future.

Now that we’ve gotten through the breakdown of methodology and explanation of my backing, here’s the ranking of each skill, from most important to least important based on their correlation and slope:

  1. Fastball Spin Rate (pushed ahead by a slightly steeper slope)
  2. Fastball Velocity
  3. Slider Spin Rate
  4. Fastball Percentage Thrown
  5. Curveball Spin Rate
  6. Command
  7. Fastball-Changeup Velocity Delta
  8. Breaking Ball Percentage Thrown
  9. Changeup Percentage Thrown

It’s worth noting that after Slider Spin Rate all other variables have basically a zero effect by themselves.

As you can probably tell from the individual skills analysis, old school seems to be at a clear disadvantage. But as they always like to preach, it’s the total package that makes a player. To account for this, I used the new/old skill groupings listed above and ran multiple linear regressions for each. This basically means the computer took into account each group’s variables together and measured the relationship between them and ERA. The results were well, not that surprising: Old school got crushed again. The correlation coefficient for the Old school group was relatively tiny for multiple variables, .024, about the same as just slider spin rate. The predicted value scatter plot also shows this as the computer had no idea how to place anything and just threw everything around the mean to hedge its losses.

The new school group fared much better, having a pretty strong correlation, all things considered, at .115. The plot also showed this with a more accurate, spread out distribution and less severe errors.

The combination of the individual skill evaluations and the groups clearly show that the new-school training regimes are focusing on the more data-backed skills. This finding is no surprise as one of their main selling points is embracing data and implementing it in a useful way. While this work does show the success of these traits in the MLB, what it doesn’t take into account is whether these skills can be taught and whether they contribute to increased injury risk, two big complaints from skeptics. These might be covered in later pieces but I thought them important to mention here as well.

This analysis may seem to completely write off the skills of command, changing speeds, and mixing your pitches that old school baseball loves to glorify. But these skills absolutely have their place. As secondary skills, they are needed along with the other abilities but in general, they can’t hold by themselves. Maybe some guys can get by with just location and changing speeds, but if you are forced to choose one of the two sets of skills, the data shows you should pick new school.

Fastball velocity and pitch spin have been the main drivers of success. If you can’t hit the broad side of the barn with your pitches, they’re obviously a moot point. But studies like mine have repeatedly shown they are crucial to pitching in today’s game so they should be a focus of player development.

 

 

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Preview my sabermetric literature book-in-progress

Hi everyone – for the past fifteen or so years I’ve been working on writing a book length manuscript with the goal of summarizing as much of the sabermetric literature that I have been able to find.  I have finished versions of the first three chapters.  The first is an introduction directed mostly to those with little background in sabermetrics.  The second reviews work on the progress of the inning, plus a bit on the progress of the game.  The third covers material on situational factors.   All are available at the following website:

https://charliepavitt.home.blog/

I have also included a table of contents including all of the projected content.

I hope many of you find this helpful in your work.  If you want to look at any of the sources I have referenced, I have copies of most of them that I can send to you.  Please contact me at chazzq@udel.edu if you have any comments or suggestions for corrections that you think I can make in updated versions of the chapter.  I am particularly interested in any relevant material I am not aware of that you can send me.

I hope to have the fourth chapter, on strategy, completed in the next month or two.

Charlie Pavitt

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What a Drag: A Follow-up

Last week I wrote a post showing that there has been a sharp reduction in star players who have passed their 35th birthday. There was a lot of discussion about this on Twitter and elsewhere, mainly focusing on the likely explanations for my data. Most people seemed to believe the largest cause for this trend is “PED testing.” This might be correct, but I was trying to leave the speculation out of it and try to focus on what the data says.

A few people suggested that I should present the data for WAR/PA, rather than just total WAR for each age. I use WAR in studies like this (I have done many such studies showing contributions broken down by race) because I don’t want the replacement-level players to swamp the data. Which they would.

In the 1988-2017 period (30 years), there were 35,913 player-seasons. Here is a plot showing the annual average age, giving all players equal weight.

Untitled4

The same rise and fall shows up here as I showed last week. With over 1200 players every season, a drop in average age of 0.8 years in the past 12 seasons is fairly dramatic.

Of this huge pool of seasons, 70% of them are fewer than 1.0 WAR, which are (roughly speaking) replacement level. In fact, if you combine these 25,012 seasons together, they sum to less than 0.0 WAR (there is more negative value in this cohort than there is positive value).

To this end, I will “bin” the rest of the data.

Replacement (less than 1.0 WAR): 25,102 total seasons, 69.6%.

Useful (1.0 <= WAR < 3.0): 7210 seasons, 20.1 %

Good (3.0 <= WAR < 7.0): 3401 seasons, 9.5%

Great (7.0 <= WAR): 290 seasons, 0.8%

These bin choices are mostly arbitrary—Tom Tango specifically asked on Twitter whether there are fewer “old” players between 1 and 3 WAR, so I thought I might as well created a few other bins.

Now I will just show the average age of the players in each of these bins.

Untitled5

 

For the best cohort (shown in blue) I combined the “good” and “great” seasons, meaning that the line shows all seasons of at least 3.0 WAR. I do this because there are relatively few great seasons, and the “great” line becomes somewhat meaningless.

Although all four cohorts show the same rough trend, the replacement players tend to be younger (at least until recently), and the average age of the good and great cohorts both drop fairly dramatically between 2005 and 2009.

When added to the post from last week, it seems clear that the contributions of older players has shrunk dramatically in the past decade, and this is true across all levels of quality.

Finally, there was some speculation that the data I showed was partly due to teams deliberately playing younger players (to save money).  Its strikes me that the players most likely to be affected by salary-based attrition would be the replacement level players, but this is the part of the roster that has aged the least.  With the important caveat that teams do not know — in advance — how good their players are going to perform, it does not seem as if they are deliberately employing young players any more than they should.

 

 

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Free Psychometric Scouting Webinar

SABR Friends,

This next Thursday, Sept. 20th,  I will be co-hosting a webinar entitled, The Mindset Science of Playing at the Next Level. It is sponsored by my new venture, Diamond Scouting, Psychometrics. Todd Thomas, Diamond’s Director of Scouting, will be hosting the event.

The many benefits of psychometric scouting and player evaluation will be covered. We will explain how quantifying player makeup, mindset, and instincts can easily identify future All-Stars, that could be overlooked or missed altogether.

The Webinar is free and I would be honored to have the SABR Statistical Analysis Committee join in and share in the discussion. Please pass the word. I am looking forward to having you and the members join us. Here is the link to register, https://t.co/tj4EOuVT5K.

Hope to see you there,

Bill Bagley
SABR Member
Psychometrician

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Tom Ruane: Fun with Retrosheet Data

Yesterday Tom Ruane posted a note to the Retrosheet mailing list about his latest research: teams who score the most (or the fewest) runs with a specific number of hits.

You can read Tom’s most recent articles here.

Tom has been a Retrosheet volunteer and board member for many years, and over the past decade has written dozens of articles on things he has gleaned from Retrosheet data.

You can see Tom’s archive here.

WARNING: his articles are very addictive.