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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.

Pro-Football-Reference.com 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, Baseball-Reference.com, Basketball-Reference.com, CBB at Sports Reference, CFB at Sports Reference, Data, FAQ, Features, Hockey-Reference.com, Pro-Football-Reference.com, SRS, Stat Questions, Statgeekery, Uncategorized

P-F-R Position Abbreviation Glossary

Posted by Neil on October 1, 2013

We often get emails about some of the more... let's say "esoteric" positional abbreviations in the P-F-R database (the most confusing of which date back to the earliest days of professional football). Here's a handy guide in case you ever get stuck wondering what in the world some of these positions are:

Comments Off | Posted in Announcement, FAQ, Pro-Football-Reference.com, Stat Questions, Trivia

Everything You Wanted to Know About Wins Above Replacement (WAR)

Posted by Neil on June 5, 2013

With Wins Above Replacement (WAR) gaining more prominence since last season, it's a good time to remind everyone about our WAR 2.2 guide:

  • Baseball-Reference.com WAR Explained - This explains the basics of WAR, the philosophy behind the stat, and some of the changes we made to the formula the past few years.
  • Weighted Runs Above Average (wRAA) Explained - This describes how wRAA, the core hitting stat of WAR, is computed.
  • Position Player WAR Calculations and Details - This explains how we turn wRAA, Baserunning & Double-Play Runs, Fielding Runs, Positional Adjustment Runs, and Replacement level Runs into WAR for position-players.
  • Pitcher WAR Calculations and Details - This explains how we take a pitcher's runs allowed & innings pitched, and turn it into WAR.
  • Converting Runs to Wins - This further describes the process by which Runs Above Replacement (for both pitchers and position players) are converted into Wins Above Replacement.
  • WAR Comparison Chart - This chart shows the differences between the WAR found here at Baseball-Reference and those from a variety of other sources, including FanGraphs & Baseball Prospectus.
  • WAR Data Downloads - If you want to download the raw WAR data yourself and play around with it, you can also get the Batting and Pitching data from our site.

Many of the questions we typically get about WAR can be answered by reading the guide linked above.

8 Comments | Posted in Advanced Stats, Announcement, Baseball-Reference.com, Data, FAQ, Features, Stat Questions, Tips and Tricks, WAR

Best NBA Scoring Duos in One Game (Mailbag)

Posted by Neil on March 5, 2013

We get asked this question a lot, and unfortunately it's not currently possible to find the answer via the Play Index, so I decided to put up a leaderboard once and for all. The most recent version of the question comes from BBR user Reggie:

I’ve seen references on here to games where two teammates scored 40 or more in the same game. But which teammates combined to score the most points in any game? [...]

One game that I remember very well as a Mavericks fan, which is often overlooked, was the overtime game they won at Chicago on November 13, 1994. That was the game where Jamal Mashburn hit for 50 and Jim Jackson went for 38, the two scoring 88 of the 124 points that night in the four point Mavs victory.

Since we have complete box scores (1985-86), here are the 200 most potent scoring duos in a single NBA game:

5 Comments | Posted in Announcement, Basketball-Reference.com, FAQ, Leaders, Trivia

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