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A number of big things caught my attention in this book.
q Stealing bases was too risky as outs were very valuable and shouldn’t be given away.
q Sacrifice bunting was an overall negative to the production of runs.
q On base percentage is under valued (at least at the time of writing). The combination of OPS (on base plus slugging) is a great formula for giving value to players. However, the formula could still be improved.
Personal Quest to Validate
As a statistician who uses regression analysis and analyses statistics all the time, I wanted to use baseball stats to validate some of the conclusions in the book. I wanted to see how easy it was to manipulate these results and see for myself. The nice thing about baseball stats is that you can get great results using simple models. Things are generally linear when looking at team stats. I took a look at every team’s stats from 2001 to 2008 to see how these years might relate to the stats used in Moneyball.
Stealing Bases
One of the biggest things Billy Beane made clear in Moneyball was that the stolen base was over-rated and was too risky to be used if you want to maximize your run totals. He estimated that the stolen base percentage had to be 70% just to break even. Any less than that and you hurt yourself. From 2001 to 2008 both AL and NL teams stole at very similar frequencies and the overall stolen base percentage was 70%.
I ran a regression analysis to predict run totals in MLB. Using walks, strikeouts, singles, doubles, triples, HRs, stolen bases, caught stealing as well as both sacrifice flies and sacrifice bunts, I come up with a simple formula that gives weight to each variable. This formula actually indicates that the break-even point was actually 75%. That means most teams were hurting themselves by stealing rather than increasing their run totals. The number actually is higher in the American League (AL) if you run the study separately. AL clubs break-even point is just above 80%. Only 6 teams out of 112 achieved this in the AL over this time period. Those clubs should listen to Billy Beane. Stealing bases is risky but it’s especially risky in the AL.
Sacrifice Bunting
This is a bit of a gray area. In general, sacrifice bunting appears to have a negative impact on scoring runs. This shows up in the formula and indicates that is costs you almost 0.34 runs per at bat when used. Just looking at the formula scoring chart developed by Carl Morris (see below) we can make at least one basic deduction of the simple sacrifice bunt. Many times a manager uses the sacrifice bunt to move the runner from first base with nobody out to second base. They are willing to sacrifice one out in order to score one run. The chart indicates that the average number of runs scored in a man on first, nobody out scenario is 0.907 runs. With a runner on 2nd base and one out, the average run total drops to 0.720 runs. And we are also assuming that the sacrifice bunt is very successful.
Hardball Times (http://www.hardballtimes.com/main/ar...eet-surrender/) writes a great article on this and notes that the sacrifice bunt is actually useful in some scenarios. It’s usually a good risk when you only need one run and there is nobody out. Other than that it’s a bad idea. This is based on the fact that late in the game (bottom of the 9th in a tie game), teams aren’t concerned about maximizing run totals. The only thing that matters is minimizing the odds of not scoring at all. In these scenarios, it’s actually still a good thing. Middle of the game scenarios would seem to make little sense to perform sacrifices.
OPS
The reliance on OBP (on base percentage), Slugging percentage and OPS (on base plus slugging) is evident in the book. The Oakland A’s value these as not only good things (who doesn’t) but that some of these things, particularly OBP, are under valued and therefore something to exploit and take advantage of. The A’s couldn’t afford the fast players or the huge home run hitters so they chose to find bargains in areas that could really help the team.
My one problem with some of the reliance on these stats is that these stats are fundamentally flawed when determining the true value of players. OBP weights everything the same. Slugging percentage uses a very simple total base formula. Is a double worth two singles? A home run worth four singles or is the relationship a bit more complex? The weights used in slugging percentage are actually pretty good although singles are over valued by the formula I come up with. There is no doubt OPS is significant. A team’s OPS is extremely correlated with runs scored. But if we really want to come up with a stat to measure player worth, shouldn’t stolen bases and strikeouts also factor in? While stolen bases are very risky, some great base stealers ARE worth it. Furthermore, a strikeout is considered a sin to Billy and also shows up as a significant negative in the computer. Paul DePodesta claimed that one point of .obp was three times the value of slugging percentage but I didn’t see any evidence of this. I would love to know how he calculated that statistic.
An average strikeout to walk ratio is 2.3:1. A strikeout is damaging so reducing them is important. A walk is almost as good as a single while a double is worth around two singles. Home runs are worth almost 5 times a single – not just 4 times as slugging percentage uses. In any event, using the numbers for walks, strike outs, singles through home runs as well as stolen bases and caught stealing, I come up with a personal overall value indicator stat (OVI). It’s not really simple but here are the Top 45 OVI guys in baseball based on their 2008 season. More weight is given to extra base hits while strikeouts are a negative.
No matter what system you use, Albert Pujols put up fantastic numbers in 2008. Some guys don’t look as good on the OVI compared to OPS. Milton Bradley (4 to 12), Josh Hamilton (16 to 24) and Adam Dunn (17 to 28) don’t fare as well near the top. Other players like Carlos Beltran (33 to 19), Grady Sizemore (31 to 22) and Nate McClouth (53 to 32) jump up nicely. Once you get outside the Top 10 players, the numbers do become much closer and arguments are much smaller. The biggest jump is clearly from Pujols to everyone else.
Code:
RK OPS RK OVI PLAYER TEAM BA OBP SLG OPS OVI 1 1 Albert Pujols STL 0.357 0.462 0.653 1.114 0.707 2 2 Chipper Jones ATL 0.364 0.470 0.574 1.044 0.623 3 3 Manny Ramirez BOS/LAD 0.332 0.430 0.601 1.031 0.618 8 4 Carlos Quentin CHW 0.288 0.394 0.571 0.965 0.607 5 5 Lance Berkman HOU 0.312 0.420 0.567 0.986 0.600 9 6 Mark Teixeira LAA/ATL 0.308 0.410 0.552 0.962 0.591 7 7 Alex Rodriguez NYY 0.302 0.392 0.573 0.965 0.588 6 8 Ryan Ludwick STL 0.299 0.375 0.591 0.966 0.578 15 9 Aubrey Huff BAL 0.304 0.360 0.552 0.912 0.575 10 10 Kevin Youkilis BOS 0.312 0.390 0.569 0.958 0.571 11 11 Matt Holliday COL 0.321 0.409 0.538 0.947 0.569 4 12 Milton Bradley TEX 0.321 0.436 0.563 0.999 0.564 12 13 Hanley Ramirez FLA 0.301 0.400 0.540 0.940 0.563 14 14 Chase Utley PHI 0.292 0.380 0.535 0.915 0.562 13 15 David Wright NYM 0.302 0.390 0.534 0.924 0.558 20 16 Brian McCann ATL 0.301 0.373 0.523 0.896 0.557 23 17 Ryan Braun MIL 0.285 0.335 0.553 0.888 0.557 22 18 Ian Kinsler TEX 0.319 0.375 0.517 0.892 0.552 33 19 Carlos Beltran NYM 0.284 0.376 0.500 0.876 0.548 27 20 Jermaine Dye CHW 0.292 0.344 0.541 0.885 0.548 24 21 Miguel Cabrera DET 0.292 0.349 0.537 0.887 0.544 31 22 Grady Sizemore CLE 0.268 0.374 0.502 0.876 0.543 25 23 Vladimir Guerrero LAA 0.303 0.365 0.521 0.886 0.543 16 24 Josh Hamilton TEX 0.304 0.371 0.530 0.901 0.543 32 25 Alfonso Soriano CHC 0.280 0.344 0.532 0.876 0.541 18 26 Aramis Ramirez CHC 0.289 0.380 0.518 0.898 0.540 21 27 Jason Bay BOS/PIT 0.286 0.373 0.522 0.895 0.538 17 28 Adam Dunn CIN/ARI 0.236 0.386 0.513 0.898 0.535 41 29 Carlos Delgado NYM 0.271 0.353 0.518 0.871 0.534 35 30 Pat Burrell PHI 0.250 0.367 0.507 0.875 0.533 28 31 Ryan Howard PHI 0.251 0.339 0.543 0.881 0.532 53 32 Nate McLouth PIT 0.276 0.356 0.497 0.853 0.531 44 33 Dustin Pedroia BOS 0.326 0.376 0.493 0.869 0.529 39 34 Justin Morneau MIN 0.300 0.374 0.499 0.873 0.527 34 35 Jason Giambi NYY 0.247 0.373 0.502 0.876 0.527 26 36 Andre Ethier LAD 0.305 0.375 0.510 0.885 0.526 38 37 Evan Longoria TAM 0.272 0.343 0.531 0.874 0.525 30 38 Prince Fielder MIL 0.276 0.372 0.507 0.879 0.520 49 39 Curtis Granderson DET 0.280 0.365 0.494 0.858 0.518 60 40 Jose Reyes NYM 0.297 0.358 0.475 0.833 0.517 40 41 Adrian Gonzalez SDG 0.279 0.361 0.510 0.871 0.515 47 42 Jim Thome CHW 0.245 0.362 0.503 0.865 0.514 51 43 Troy Glaus STL 0.270 0.372 0.483 0.856 0.511 37 44 Joey Votto CIN 0.297 0.368 0.506 0.874 0.510 67 45 Mike Jacobs FLA 0.247 0.299 0.514 0.812 0.507 46 46 Xavier Nady NYY/PIT 0.305 0.357 0.510 0.867 0.505 19 47 Nick Markakis BAL 0.306 0.406 0.491 0.897 0.504 52 48 Brian Giles SDG 0.306 0.398 0.456 0.854 0.504 84 49 Jimmy Rollins PHI 0.277 0.349 0.437 0.786 0.503 43 50 Magglio Ordonez DET 0.317 0.376 0.494 0.869 0.502
I loved this book. It's great food for thought and I know I will think twice about wanting the Tigers to be a better base stealing team. Forget manufacturing runs. Chicks did the long ball!
# 1
rspencer86 @ Mar 2
I finished reading Moneyball several months ago and loved it as well. I love revolutionary ideas in sports, such as the people who say that NFL teams punt the ball far too often in 4th and short situations.
# 2
BlyGilmore @ Mar 2
Man you're just getting to this?
One of the interesting things about Moneyball IMO is how some of its ideas and beliefs have been absorbed into baseball's "common sense" -but some of it hasn't.
For instance you need to have a certain number of players who take pitches, work the count and generally harass pitchers with their patience. But if you have too many of these types of players on your team you turn into more of a men's softball team than a baseball team.
The thing that also tends to go unnoticed or not remembered is that Beane and Company went this route because players like Scott Hatteberg were undervalued and underappreciated. So it let a team with limited resources snatch them up.
As the ideas became more accepted, this wasn't the case any more and you saw the A's moving to other strategies (namely hoarding good young arms in their farm system).
One of the interesting things about Moneyball IMO is how some of its ideas and beliefs have been absorbed into baseball's "common sense" -but some of it hasn't.
For instance you need to have a certain number of players who take pitches, work the count and generally harass pitchers with their patience. But if you have too many of these types of players on your team you turn into more of a men's softball team than a baseball team.
The thing that also tends to go unnoticed or not remembered is that Beane and Company went this route because players like Scott Hatteberg were undervalued and underappreciated. So it let a team with limited resources snatch them up.
As the ideas became more accepted, this wasn't the case any more and you saw the A's moving to other strategies (namely hoarding good young arms in their farm system).
# 3
rudyjuly2 @ Mar 2
I'm trying to get the one table in there but I think this is the best I can do.
I think NFL teams should be using stats to analyse past draft success and failure. With all the college stats and combine numbers, certainly you would think that some areas could be determined to be overvalued and other areas undervalued. It took baseball forever to look at stats and it was largely outside people that did the studies. Combine results are not available in such simple formats and its only recently that you can track a ton of college football player statistics on the internet. I am curious to know if any NFL team uses an approach like they have in baseball.
I think NFL teams should be using stats to analyse past draft success and failure. With all the college stats and combine numbers, certainly you would think that some areas could be determined to be overvalued and other areas undervalued. It took baseball forever to look at stats and it was largely outside people that did the studies. Combine results are not available in such simple formats and its only recently that you can track a ton of college football player statistics on the internet. I am curious to know if any NFL team uses an approach like they have in baseball.
# 4
BlyGilmore @ Mar 3
I think you can look at the Patriots as doing something similar. While teams like the Redskins and Cowboys were throwing boatloads of money at the big name free agents, the Patriots were signing veteran players who were discarded by their former teams for basically bargain prices - especially on the defensive side of the ball.
Guys like Vrabel, Rodney Harrison, Ted Washington, Junior Seau, etc. were either former stars their teams though were too old to play, or guys dumped and failures before being snatched up by the Pats.
They have done the same thing with QBs - looking for Brady/Cassel types they can snag in the later rounds and groom into good professional quarterbacks.
The difference with football though (and pretty much every sport but baseball) is you just can't accurately judge a players worth by statistics.
Guys like Vrabel, Rodney Harrison, Ted Washington, Junior Seau, etc. were either former stars their teams though were too old to play, or guys dumped and failures before being snatched up by the Pats.
They have done the same thing with QBs - looking for Brady/Cassel types they can snag in the later rounds and groom into good professional quarterbacks.
The difference with football though (and pretty much every sport but baseball) is you just can't accurately judge a players worth by statistics.
# 5
rudyjuly2 @ Mar 3
Football is tough since stats are so dependent on your teammates. But I would be very curious if you could study college players combine numbers together with college statistics and see if you could find things that would indicate success and/or failure. Minimizing risk in the draft was a big thing by Billy Beane and I would have to think the same thing would be possible for certain positions in the NFL draft.
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