One of the big changes in baseball recently is the increased use of sabermetrics by both teams and the media. One thing that puzzles me is that you don’t nearly hear as much about these “new style” statistics in other sports. In football (either form) or basketball, statistics are both much less mentioned and almost always involve traditional stats. Do other sports use sabermetric-like statistics? If so what are they? Do other sports just not lend themselves to statistics?
Baseball is clearly the most favorable sport for statistical analysis since, for the most part, players operate independently (with some exceptions, mostly on defense). But there has been a lot of work done in the NBA over the last 10-15 years. Much of this is proprietary (owned by the NBA teams and their analytics departments) since gathering/processing this data is time intensive and not cheap. Still, there’s a lot out there in the public domain. The major “breakthrough” of sorts is the embracing of shooting efficiency indicators (such as true shooting percentage), which is kind of the analog to on-base percentage in baseball. This leads to a lot of similar debates as we have seen in baseball:
“What do you mean this 30 point-per-game scorer is not a good shooter? He scores 30 points a game!”
“What do you mean this .300 hitter isn’t a good hitter? He’s hitting .300!”
Some other basketball stats which have become popular are pace factors (correcting stats for the pace at which teams play–more possessions = more counting stats, so these are not always reliable), rebounding percentages (expressing rebounds as the percent of total rebounds rather than a raw number which is susceptible to distortion based on pace), and an increased emphasis on turnovers.
I’m not an expert on football analytics but it seems to be the ultimate team game making isolating a given player’s contributions difficult at best. Most of the stuff I’ve seen related to the NFL was analysis of the team, not individual players.
Most of being a cricket fan is discussing statistics about each and every aspect of the game.
I thought this was a pretty interesting article on the rise of Sabermetric style stats in basketball as it relates to a particular player: The No-Stats All-Star.
I know one NFL team that is using a lot more stats because they hired my company to help them out. Cannot say who it is.
As with baseball, this may be a reflection of the relative tedium of actually watching the game. ;)
In the NFL, there have been several attempts to come up with better QB rating systems than the one which the NFL has used since the 1960s (if not earlier). The current system has always been pretty arbitrary, and probably doesn’t reflect the fact that the passing game is very different now than it was 50 years ago. As of this point, none of those alternate ratings have really caught on.
Cold Hard Football Facts is one of a number of sites which takes a sabermetric-style view of the NFL.
I’m convinced that cricket was actually designed by accountants and statisticians. We have score sheets from games going back to the 18th century and ball by ball records from some games in the 19th. The absurdly comprehensive database at cricinfo covers every test match ever played since 1877, every one day international and 20/20 international.
Want to find out which left arm spinner has taken the most wickets at Sabina Park in Jamaica? Here you go. Which Australian batsman has scored the most runs in Pakistan? Not a problem.. And the current coverage is insanely detailed. Thanks to Hawkeye you can relive every ball of Alastair Cook’s epic innings in the current match against India.
However there aren’t any equivalents to baseball’s WAR or VORP in cricket. Batting and bowling averages in cricket are actually useful stats, and the most recent new stat added was strike rates for bowlers and batters in the 80s or 90s.
If you count politics as a sport, of course, we have to mention Nate Silver.
True shooting percentages are so obvious they’re kind of brilliant. Efficiency ratings on offense and defense like points allowed per 100 possessions and measurements on a team’s pace of play have also come to supplement or replace raw statistics like points scored or allowed. In a way this stuff is even more fascinating in basketball because players can affect each others’ games in so many different ways through factors like spacing or the tendencies of different players.
Basketball box scores are now showing “+/-” which is the net points a team scores while that player is in the game. Real statheads regress those numbers based on lineups and minute distribution to attempt to isolate that particular player’s impact variable on the game. Then adjust for actual minutes played and the possessions per game as mentioned above.
this is called RAPM - Regularized Adjusted Plus Minus and it’s pretty much the standard for basketball nerds as how a player does.
There are other aggregate scores based on regular box score stuff like PER (player efficiency rating) and game score and other stuff too but it’s inexact and biases, notably post players and hyperefficient role players.
PER is normalized to that particular season so it’s tough to compare eras.
Another reason why baseball lends itself to stats is that there simply is more of it. Basketball didn’t record blocks or differentiate between offensive and defensive rebounds for the 60’s and only incorporated the 3 point line in the 80’s.
I remember a Wired article from a long time back about a statistician who applied his skills to horse-racing. It turned out there were a bunch of variables that bookies didn’t take into account (the one I remember is the weather the day before the race, which apparently had an effect on the track, but there was a longish list). By making a large database of how these effected a horse, they could get an edge on betting and, with enough bets, turn a predictable profit.
When the NBA ref point fixing scandal came out I remember there being an article of some guy who’s a professional NBA bookie who did more or less the same thing. He didn’t look at player stats but the outside factors. Who was reffing, home/away, off a back-to-back, weekday vs weekend, and came up with an algorithm. He said he spotted an anomaly in a few games that didn’t cover like they should but within tolerance.