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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

Explore PFR’s Super Bowl Section

Posted by Mike Lynch on January 23, 2015

If you haven't seen the Super Bowl Section of our site yet, you should head over and check it out right now, because we think it's pretty awesome. Here are some of the features you'll find once you get there:

And best of all is the Super Bowl Play Finder tool. It's similar to the Play Index Game Play Finder tool, but limits your searches strictly to Super Bowls and allows you to search through every play of all 48 previous games. Some examples:

Our Player Game Finder and Team Game Finder are also unique tools for Super Bowl research. You can use them to find things like every 100-yard rusher in Super Bowl history, the largest point spreads in Super Bowl history or the highest over/unders. You can also identify trends such as the record of the team leading after one quarter (26-11), at halftime (36-10) and through three quarters of play (39-8).

So go ahead and try the Super Bowl Section right now -- it's free, easy to use, and best of all, with it you'll be able to dominate anyone who challenges you in Super Bowl trivia.

UPDATE: We have also added a Super Bowl filter to our Drive Finder, which allows users to find things such as the longest drives in any Super Bowl since 1998 or the fastest Super Bowl touchdown drives since 1998.

1 Comment | Posted in Announcement, Data, Features, History, Play Index, Playoffs, Pro-Football-Reference.com, Super Bowl, Uncategorized

Punt Statistics Added to Team Game Finder

Posted by Mike Lynch on January 9, 2015

Just wanted to quickly note that we have added Punts, Punting Yards & Yards per Punt to the Team Game Finder in the Play Index. The data is complete back to 1950, but is spotty before that. We apologize to Doug Drinen for adding more punting stats, but encourage users to enjoy the new queries.

Comments Off on Punt Statistics Added to Team Game Finder | Posted in Announcement, Features, Play Index, Pro-Football-Reference.com, Uncategorized

Additions to Team Streak Finders

Posted by Mike Lynch on October 7, 2014

Just wanted to post a quick blurb that we have made a few recent additions to the Team Streak Finder:

5 Comments | Posted in Announcement, Data, Features, Play Index, Pro-Football-Reference.com, Uncategorized

Span of Games Searches Enhanced in Play Index Game Finders

Posted by Mike Lynch on September 12, 2014

Just a quick post to announce that we've rolled out an enhancement to the Play Index Game Finders (comprised of Player Batting, Playing Pitching, Team Batting & Team Pitching Game Finders).

Previously, subscribers could customize their searches by a team's first "x" games. Now, you can also select a player's first "x" career games or a span of career games from "x" to "y." Additionally, team games can also be searched using any span from "x" to "y" within a season.

If the above made little sense, here's a few concrete examples of searches that can now be completed:

We hope you enjoy these new tools as much as we do. If you're not already a subscriber, but are interested in the Play Index, you can subscribe here for less than a dime per day.

13 Comments | Posted in Announcement, Baseball-Reference.com, Data, Features, Play Index, Uncategorized

Your One-Stop Resource for 2014 College Football Data

Posted by Mike Lynch on August 29, 2014

With the 2014 college football season upon, just a few reminders on where to find info for this season:

If you have any questions about where to find something or if you encounter any issues, please let us know here.

2 Comments | Posted in Announcement, CFB at Sports Reference, Data, Play Index, Uncategorized

Dive Into Our Stats with the Play Index This Season

Posted by Mike Lynch on August 29, 2014
The Play Index is the name we use to refer to PFR's collection of top-notch research tools. Read below for more information on some of the applications we have made available to you.

Search all plays from 1998 to today to find performances that match your criteria. Answer questions like…

Player Season Finder

Search through player season stats spanning from 1920 to today for single seasons or combined seasons that match your criteria. Answer questions like…

The answers to these questions and many, many more are at your fingertips using the Player Season Finder.

Player Game Finder

Search through player game logs spanning from 1960 to today for games that match your criteria. You can find…

With the Player Game Finder, the possibilities are almost endless.

Player Touchdown Finder

Search through every touchdown scored from 1920 to today for scores that match your criteria. Did you know…

Impress your friends and come up with your own "Did You Know" with a little help from the Player Touchdown Finder.

Team Game Finder

Find team games or seasons matching certain criteria. Did you know…

Player Streak Finder

Find the longest player streaks matching certain criteria. Did you know...

Team Streak Finder

Find the longest team streaks matching certain criteria. Did you know...

Super Bowl Play Finder

Search through every play in Super Bowl History.

Draft Finder

Search through every NFL and AFL draft pick.

Find head-to-head results and also find games matching a particular score.

Comments Off on Dive Into Our Stats with the Play Index This Season | Posted in Announcement, Features, History, Play Index, Pro-Football-Reference.com, Uncategorized

New Football Features for 2014

Posted by Mike Lynch on August 29, 2014

With football season once again upon us, we figured users that have been away from the sites in the offseason could use a refresher on some changes/updates. We remained hard at work in the offseason rolling out a series of new features & improvements for users to enjoy this season:

We hope everyone finds these new features useful this season.

 

15 Comments | Posted in Announcement, Data, Features, Pro-Football-Reference.com, Uncategorized

The Complicated History of RBI

Posted by Mike Lynch on August 6, 2014

If you have spent an extended amount of time on Baseball-Reference, you have likely noticed that some of our RBI totals do not match what you will see on some other sites. A notable example would be none other than George Herman Ruth. We list him with 2,214 career RBI, with a career high of 168 in 1921. Many sources, however, credit him with 2,213 career RBI and a season high of 171 in 1921.

How can there be any dispute over how many runs the most iconic player in the history of baseball drove in?

We're glad you asked.

It might come as a surprise to some, but RBI was not an official statistic until 1920, which was Ruth's first season with the Yankees. And even then, Rule 86, Section 8 was remarkably vague from 1920-30, instructing official scorers only that:

"The summary shall contain: The number of runs batted in by each batsman."

That left plenty of room for interpretation of the scoring rule. In the absence of a strict definition, official scorers across the league were inconsistent in what they considered an RBI. This inconsistency polluted numbers for a decade, despite the fact that the statistic was finally "official."

It wasn't until 1931, when Rule 70, Section 13 made the definition more explicit, that a uniform policy for counting RBI existed:

"Runs Batted In are runs scored on safe hits (including home runs), sacrifice hits, outfield put-outs, infield put-outs, and when the run is forced over by reason of the batsman becoming a base-runner. With less than two outs, if an error is made on a play on which a runner from third would ordinarily score, credit the batsman with a Run Batted In."

While this definition has seen some tweaks over time, for the first time official scorers had a clear definition of what should count as an RBI (though tabulation errors were still an issue in a pre-computerized era).

With RBI not tracked by official scorers, where do the pre-1920 RBI numbers come from? Here is a breakdown of the history of various RBI sources.

These RBI numbers have been used in various encyclopedias over the years and have served as the basis for further research done by SABR members. This research, where 5-7 newspaper accounts are looked at for each game in order to deduce RBI, often proves earlier reconstructions (and official totals) wrong. This leads to the volatile nature of early RBI numbers. A well-detailed account of this process by SABR's Herm Krabbenhoft can be found here, showing how he meticulously worked through Ruth's career RBI totals.

These thoroughly researched corrections eventually make their way to Baseball-Reference via Pete Palmer's data after they have been sufficiently vetted, which is why you will see discrepancies between our numbers and what you see in some other places. We have full confidence that when such alterations are made, that we are putting forward the best possible data generated by countless hours of expert research.

2 Comments | Posted in Announcement, Baseball-Reference.com, Data, History, Uncategorized

Explaining the Honus Wagner Career Hits Discrepancy

Posted by Mike Lynch on July 29, 2014

As Derek Jeter continues his climb up baseball's all-time hits list, we have received several inquiries about Honus Wagner's career hit total. We list 3,420, while MLB lists him with 3,430 career hits. While the similarity of the numbers may imply a simple typo, it turns out that the reasons for the one-digit difference are not simple at all.

For an explanation of the history of this deviation, we spoke with Pete Palmer (the source for many of the statistics appearing on this site). Palmer explained that the 1969 Macmillan Baseball Encyclopedia was the genesis of the difference. At the time, official NL statistics only went back to 1903. The encyclopedia created new statistics for years prior to that and the changes were approved by an MLB committee set up to rule on various statistics for inclusion in the encyclopedia. However, the Elias Sports Bureau, which is the official statistician for Major League Baseball, never accepted the committee ruling, which leads to some differing numbers between what you see on Baseball-Reference and what you see in official MLB records. Elias, instead, has always used data from the old Spalding Guides.

Pete Palmer (and by extension Baseball-Reference) has preferred to use the Macmillan data*, because daily figures exist to back the numbers up, which allows for the statistics to be proofed for greater accuracy. Here is a year-by-year look at the difference between our totals and the Spalding totals. These are all from Wagner's pre-1903 seasons (Baseball-Reference total listed first):

  • 1897: 81, 83
  • 1898: 176, 180
  • 1899: 196, 197
  • 1900: 201, 201
  • 1901: 194, 196
  • 1902: 176, 177

Another discrepancy that some of you may notice soon is that Baseball-Reference has Cap Anson with 3,435 career hits, while MLB has him with 3,011. While many discrepancies exist with that data, the bulk of the difference is the fact that we count Anson's 423 hits in the National Association, which we believe was clearly a major league.

TL;DR version: Our hit total for Honus Wagner is not a typo. We recognize it does not align with the official total, but we believe it is the most accurate number.

For further reading on some of the issues with official totals in baseball statistics, please read this excellent 2011 post by Retrosheet's Dave Smith.

*The Macmillan data excluded a few games that were protested and replayed in the 1890s. These statistics were included in the NL stats of the day (save for the wins and losses) and Palmer has added these statistics back into the Macmillan data to reflect this.

37 Comments | Posted in Announcement, Baseball-Reference.com, Data, History, Leaders, Uncategorized

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