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What is the Little League Home Run?

Posted by Jonah Gardner on August 13, 2015

What is your favorite kind of baseball play? I imagine a lot of people would go with a classic like the dinger. Or perhaps you prefer the swinging strikeout? I imagine Red Sox fans who were in Fenway for Game 4 of the 2004 ALCS may be partial to the stolen base. No matter what it is, I'm very jealous of you, because your favorite play has an easily understood definition and mine does not:

Comments Off on What is the Little League Home Run? | Posted in Advanced Stats,, Polls, Statgeekery

Kevin Garnett vs the Trade Voltron

Posted by Jonah Gardner on July 31, 2015

Today is the 8th anniversary of the Kevin Garnett trade. Of course, there have now been 3 Kevin Garnett trades, but you know the one I'm talking about. In 2007, the Minnesota Timberwolves changed NBA history by sending KG to Boston in exchange for Al Jefferson, Ryan Gomes, Gerald Green, Sebastian Telfair, Theo Ratliff, and the picks that would be come Wayne Ellington and Jonny Flynn.

As you know, that trade went much better for Boston and KG than it did for the Timberwolves, who haven't been to the playoffs since. It's easy to criticize this trade, but Minnesota was in a difficult position for two reasons. First, Kevin Garnett is a once-in-a-generation talent, so almost no trade could have brought back a fair return. And, second, they couldn't build a Voltron out of the players they received in the trade.

While there's nothing we can do about the former, we decided to celebrate the anniversary of the trade by doing the latter. I give you, the Kevin Garnett Trade Voltron:

1 Comment | Posted in, Ridiculousness, Statgeekery

Hans van Slooten now Primary Developer for

Posted by sean on April 10, 2015

I am pleased to announce that Hans van Slooten has taken over day-to-day development of Hans has been with Sports Reference for 15 months now and moved over from hockey to baseball last month. Hans is a long-time SABR member and a very talented developer. You are likely to see a bunch of improvements this summer with Hans on the site full-time rather than me on the site half-time. He's also a dedicated Twins fan, so he will not face the unneeded distraction of a deep postseason run by his favorite team. Hans is on twitter at @cantpitch.

I'm not going anywhere. I'm still President of Sports Reference and will certainly be involved in, just not with day-to-day responsibilities. With six full-time employees, I have a bit more a management role now, and we are also launching a new site this summer and expecting to roll out some changes to all of the sites this summer.

2 Comments | Posted in Announcement,, Statgeekery

FEATURE: B-R Tonsorial Consulting Service

Posted by sean on April 1, 2015

We have an exciting feature for our users who also happen to be major league ballplayers. The new TCS® (Tonsorial Consulting Service) can help you decide new directions to take your hair style and/or facial hair without having to take the time to grow the hair first.

12 Comments | Posted in Announcement,, expire7d, Features, Statgeekery, Tips and Tricks

Heinie Zimmerman wins the retro-active 1912 NL Triple Crown

Posted by admin on March 5, 2015

One of the things I love about SABR is how dedicated (and borderline crazy) some of the researchers are and how their years and years of work can bear fruit in unexpected ways. (I'm sure I love it because I have more than a bit of that in me as well.) In next month's Baseball Research Journal, an article by Herm Krabbenhoft will show that Heinie Zimmerman had the highest RBI total in the 1912 NL, and when paired with his undisputed batting title and 14 home runs, he won the triple crown.

8 Comments | Posted in Advanced Stats, Announcement,, Statgeekery

SRS Calculation Details

Posted by Mike Lynch on March 3, 2015

One of the more common subjects for queries we receive at Sports-Reference is our SRS (Simple Rating System) figures. For some background, the first of our sites to add SRS was Pro-Football-Reference, when Doug Drinen added it to the site in 2006 and provided this excellent primer. The important thing to know is that SRS is a rating that takes into account average point differential and strength of schedule. For instance, the 2006-07 Spurs won games by an average of 8.43 points per game and played a schedule with opponents that were 0.08 points worse than average, giving them an SRS of 8.35. This means they were 8.35 points better than an average team. An average team would have an SRS of 0.0. The calculation can be complicated, but the premise is simple and it produces easily interpreted results.

However, there are some variations in the way we calculate SRS across our various sites. We'll break down these differences below. SRS: PFR's SRS is unique in that a home-field advantage is included as a part of the calculation because of the short schedule compared to the other sports (we don't want a team to look relatively weak at the halfway point because they've only played 3 of their first 8 at home, for instance). This HFA fluctuates yearly based on game results, but it is generally somewhere between 2 and 3 points (2006 being an outlier, as you'll see). Below is a look at the HFA numbers we have used since 2001. If you'd like to calculate these HFAs yourself, just sum up every team's home point differential and then divide by the total number of games played across the league that season. This data can easily be found in the Play Index for each season:

  • 2001: 2.0081
  • 2002: 2.2461
  • 2003: 3.5547
  • 2004: 2.5078
  • 2005: 3.6484
  • 2006: 0.8477
  • 2007: 2.8672
  • 2008: 2.5586
  • 2009: 2.2070
  • 2010: 1.8945
  • 2011: 3.2656
  • 2012: 2.4336
  • 2013: 3.1055
  • 2014: 2.4883

College Football SRS: Our CFB SRS does not contain a home-field advantage element, but it does have some other quirks. Most importantly, we have capped the margin of victory considered for the formula. Due to the number of mismatches seen in college football, the maximum point differential a team can be credited with in a game is 24. We also credit all wins as a minimum of plus-7 margin of victory (so if you win by 1 point, it's treated the same as a 7-point win). The same logic is applied to losses, as well. One other wrinkle for CFB is that all non-major opponents are included as one team for the sake of the ratings.

College Basketball SRS: SRS for college hoops is straight forward (no HFA & no adjusted MOV), but one item to note is that games against non-major opponents are not counted in our calculations.

MLB, NBA & NHL: All of these SRS calculations are straight forward with no adjustments for HFA and no capping of MOV. It should be noted, however, that no special consideration is given for extra-innings, overtimes or shootouts, either.

We'll close with a quick rundown of the various merits and weaknesses of SRS, from Drinen's original 2006 post. These bullet points were created to describe the system used for NFL SRS, but many of the strengths and weaknesses can applied to the other sports, as well:

  • The numbers it spits out are easy to interpret - if Team A's rating is 3 bigger than Team B's, this means that the system thinks Team A is 3 points better than Team B. With most ranking algorithms, the numbers that come out have no real meaning that can be translated into an English sentence. With this system, the units are easy to understand.
  • It is a predictive system rather than a retrodictive system - this is a very important distinction. You can use these ratings to answer the question: which team is stronger? I.e. which team is more likely to win a game tomorrow? Or you can use them to answer the question: which of these teams accomplished more in the past? Some systems answer the first questions more accurately; they are called predictive systems. Others answer the latter question more accurately; they are called retrodictive systems. As it turns out, this is a pretty good predictive system. For the reasons described below, it is not a good retrodictive system.
  • It weights all games equally - every football fan knows that the Colts' week 17 game against Arizona was a meaningless exhibition, but the algorithm gives it the same weight as all the rest of the games.
  • It weights all points equally, and therefore ignores wins and losses - take a look at the Colts season. If you take away 10 points in week 3 and give them back 10 points in week 4, you've just changed their record, but you haven't changed their rating at all. If you take away 10 points in week 3 and give back 20 points in week 4, you have made their record worse but their rating better. Most football fans put a high premium on the few points that move you from a 3-point loss to a 3-point win and almost no weight on the many points that move you from a 20-point win to a 50-point win.
  • It is easily impressed by blowout victories - this system thinks a 50-point win and a 10-point loss is preferable to two 14-point wins. Most fans would disagree with that assessment.
  • It is slightly biased toward offensive-minded teams - because it considers point margins instead of point ratios, it treats a 50-30 win as more impressive than a 17-0 win. Again, this is an assessment that most fans would disagree with.
  • This should go without saying, but - I'll say it anyway. The system does not take into account injuries, weather conditions, yardage gained, the importance of the game, whether it was a Monday Night game or not, whether the quarterback's grandmother was sick, or anything else besides points scored and points allowed.


2 Comments | Posted in Announcement,,, CBB at Sports Reference, CFB at Sports Reference, Data, FAQ, Features,,, SRS, Stat Questions, Statgeekery, Uncategorized

Corrections to 1994 Pacers Playoff Statistics

Posted by Mike Lynch on June 20, 2014

It recently came to our attention that there are some conflicting scores listed by various sources for Game 6 of the 1994 Pacers/Hawks Eastern Conference Semifinals series. This prompted some digging here. As a result, we have changed our score, from 97-79, to a 98-79 final. The extra point has been awarded to Antonio Davis for making 1 of 2 FTA in the 4th quarter.

However, this also led to our discovery that some of the "official" playoff totals for the 1993-94 Pacers are not correct. They are "officially" credited with 1,444 points in the 1994 Playoffs, but if you add up all of their scores, you will get 1,445. Similarly, Antonio Davis is "officially" credited with 134 points in the 1994 Playoffs, but if you add up his game logs, you will get 135.

It is our belief that the 98th point we were missing from our Game 6 score has gone unaccounted for in NBA stat totals for the last 20 years. As such, we are adjusting the Playoff scoring totals for the 1994 Pacers. The team will now be credited with 1,445 Playoff points (rather than 1,444) and Antonio Davis with 135 Playoff points (rather than 134). Additionally, 1 FTM and 2 FTA have been added to the Pacers' total (all credited to Davis).

Thanks to this correction, Davis has moved past Larry Foust for sole possession of 219th place on our NBA all-time Playoff scoring list. Congratulations, Antonio!


2 Comments | Posted in Announcement,, Data, History, Playoffs, Statgeekery

KBO Stats back to 1999 –

Posted by sean on June 2, 2014

It's a small thing, but we are always trying to push our data as far back as possible and we've added (thanks to Brian Cartwright and Ted Turocy) Korea Baseball Organization stats back to 1999. This doesn't get us all of Seung-Yeop Lee's career stats, but pretty close.

Enjoy. Cuban stats are up next.

Korean Baseball Organization KBO Statistics and History -

2 Comments | Posted in Announcement,, Statgeekery

Team Positional Comparison Tool–in our opinion it’s pretty cool –

Posted by sean on May 30, 2014

Positional differences and performance by position are interesting ways to look at team performance. For instance as Phillies fan I know that CF and LF are hurting the Phillies, but just how much are they hurting relative to the league. This tool answers that question and many many more. (answer: a whole lot of hurt)

You select a year, the league and a stat, and we rank every team at each position by that stat and in some cases a summary of positions. This uses our positional splits to allot stats to positions or in some cases a prorated assignment of WAR and related stats by time played at each position. Now in one table you can see the worst or best team-positions in baseball.

There are some additional tools that allow you to highlight a particular team and mousing over a table cell informs you the players involved in producing that value.

I like this output so much I've also added it to our base league pages just below the pitching stats.

2014 AL League page

Team Comparison Tool -

6 Comments | Posted in Advanced Stats, Announcement,, Features, Statgeekery

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