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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

Fielding Independent Pitching (FIP) added to Baseball-Reference.com

Posted by sean on April 17, 2014

Last night, I added FIP (short for Fielding Independent Pitching) to the site. This is a sabermetric stat for pitchers that approximates ERA without the effect of their team's fielding ability. FIP actually correlates to future ERA better than ERA itself making it a superior indicator of future performance.

The idea is that the pitcher most directly controls the number of walks, home runs and strikeouts that occur and that the batters and fielders have a bigger say on whether balls in play are turned into outs and that most pitchers' Batting Average on Balls in Play (BAbip) reverts to a league average from one year to the next.

FIP is (13*HR + 3*(BB+HBP) - 2*SO)/IP + Constant(year). The constant is set so the yearly avg FIP equals the yearly avg ERA.

FIP can be looked at exactly like ERA and is scaled to exactly the same league average as ERA, but it's range will be slightly smaller.

Often a player with a low FIP and high ERA will improve, while a low ERA and high FIP indicates a likely regression as more hits start falling. I've placed FIP next to ERA to make this comparison more obvious, but if it begins making the ERA lookup too hard, I may move it further right on the pitching tables.

I've also added FIP and K% to the Play Index Season Finder. Right now, I don't believe we will add xFIP given the inconsistency in batted ball data, but that could change.

Player with big gap in FIP and ERA: Ricky Nolasco.

10 Comments | Posted in Advanced Stats, Announcement, Baseball-Reference.com, Most Wanted, Stat Questions

BBR Feature Add: Practice Shot Charts

Posted by sean on April 1, 2014

Practice Shot Charts.

NBA.com has really upped the level of detail they've added, so we're trying to respond with the latest and greatest data we can get. We've recently come to an agreement with a number of franchises for access to their practice film and have begun breaking down scrimmage data.

Practice shot charts seem to us the next frontier in basketball analytics. This data will show you who's improving, who isn't, and who is working on new aspects of their game. It's especially useful for players without a lot of playing time.

Before we roll it out sidewide, we've prepared some sample charts.

3 Comments | Posted in Advanced Stats, Announcement, Basketball-Reference.com, expire10d, Most Wanted, Stat Questions, Statgeekery, Uncategorized

Where Does Florida State Rank Entering the BCS Championship Game?

Posted by Neil on December 9, 2013

Chase asked where FSU's +42.3 pre-bowl PPG differential ranked among all BCS-bowl teams historically, so I thought I'd run a quick database search:

Year School Pre-Bowl MOV Bowl
2013 Florida State (acc) +42.3 BCSCG (vs. Auburn)
2005 Texas (big-12) +36.3 Rose (W 41-38 vs. Southern California)
2001 Miami (FL) (big-east) +33.8 Rose (W 37-14 vs. Nebraska)
2008 Florida (sec) +32.3 BCSCG (W 24-14 vs. Oklahoma)
2000 Florida State (acc) +32.2 Orange (L 2-13 vs. Oklahoma)
2013 Baylor (big-12) +32.1 Fiesta (vs. Central Florida)
2010 Texas Christian (mwc) +31.9 Rose (W 21-19 vs. Wisconsin)
2010 Oregon (pac-10) +30.9 BCSCG (L 19-22 vs. Auburn)
1999 Virginia Tech (big-east) +30.8 Sugar (L 29-46 vs. Florida State)
2003 Oklahoma (big-12) +30.3 Sugar (L 14-21 vs. Louisiana State)
2008 Southern California (pac-10) +29.8 Rose (W 38-24 vs. Penn State)
2001 Florida (sec) +29.7 Orange (W 56-23 vs. Maryland)
2008 Oklahoma (big-12) +29.5 BCSCG (L 14-24 vs. Florida)
2012 Oregon (pac-12) +28.8 Fiesta (W 35-17 vs. Kansas State)
2005 Southern California (pac-10) +28.7 Rose (L 38-41 vs. Texas)
2007 Kansas (big-12) +28.3 Orange (W 24-21 vs. Virginia Tech)
2009 Texas Christian (mwc) +28.3 Fiesta (L 10-17 vs. Boise State)
2011 Louisiana State (sec) +27.9 BCSCG (L 0-21 vs. Alabama)
2012 Alabama (sec) +27.8 BCSCG (W 42-14 vs. Notre Dame)
2008 Penn State (big-ten) +27.8 Rose (L 24-38 vs. Southern California)
2011 Wisconsin (big-ten) +27.6 Rose (L 38-45 vs. Oregon)
2013 Alabama (sec) +27.4 Sugar (vs. Oklahoma)
2000 Miami (FL) (big-east) +27.2 Sugar (W 37-20 vs. Florida)
2011 Alabama (sec) +27.2 BCSCG (W 21-0 vs. Louisiana State)
2009 Boise State (wac) +26.5 Fiesta (W 17-10 vs. Texas Christian)
2010 Ohio State (big-ten) +26.1 Sugar (W 31-26 vs. Arkansas)
2006 Ohio State (big-ten) +25.9 BCSCG (L 14-41 vs. Florida)
2004 Utah (mwc) +25.6 Fiesta (W 35-7 vs. Pittsburgh)
2009 Texas (big-12) +25.5 BCSCG (L 21-37 vs. Alabama)
2008 Texas (big-12) +25.3 Fiesta (W 24-21 vs. Ohio State)
2007 Oklahoma (big-12) +25.2 Fiesta (L 28-48 vs. West Virginia)
1998 Ohio State (big-ten) +25.1 Sugar (W 24-14 vs. Texas A&M)
2013 Ohio State (big-ten) +25.0 Orange (vs. Clemson)
2012 Florida State (acc) +24.8 Orange (W 31-10 vs. Northern Illinois)
2004 Southern California (pac-10) +24.3 Orange (W 55-19 vs. Oklahoma)
2003 Louisiana State (sec) +24.2 Sugar (W 21-14 vs. Oklahoma)
2006 Boise State (wac) +23.8 Fiesta (W 43-42 vs. Oklahoma)
2002 Miami (FL) (big-east) +23.8 Fiesta (L 24-31 vs. Ohio State)
2011 Oklahoma State (big-12) +23.5 Fiesta (W 41-38 vs. Stanford)
2002 Oklahoma (big-12) +23.5 Rose (W 34-14 vs. Washington State)
2003 Southern California (pac-10) +23.4 Rose (W 28-14 vs. Michigan)
2011 Stanford (pac-12) +23.3 Fiesta (L 38-41 vs. Oklahoma State)
2009 Florida (sec) +23.2 Sugar (W 51-24 vs. Cincinnati)
2000 Oklahoma (big-12) +23.0 Orange (W 13-2 vs. Florida State)
2010 Wisconsin (big-ten) +22.8 Rose (L 19-21 vs. Texas Christian)
2011 Oregon (pac-12) +22.5 Rose (W 45-38 vs. Wisconsin)
2010 Stanford (pac-10) +22.5 Orange (W 40-12 vs. Virginia Tech)
1999 Wisconsin (big-ten) +22.5 Rose (W 17-9 vs. Stanford)
2004 Oklahoma (big-12) +22.4 Orange (L 19-55 vs. Southern California)
2006 Louisville (big-east) +22.3 Orange (W 24-13 vs. Wake Forest)
2003 Kansas State (big-12) +22.3 Fiesta (L 28-35 vs. Ohio State)
2004 Auburn (sec) +22.3 Sugar (W 16-13 vs. Virginia Tech)
2007 Hawaii (wac) +22.0 Sugar (L 10-41 vs. Georgia)
2012 Northern Illinois (mac) +21.8 Orange (L 10-31 vs. Florida State)
1999 Nebraska (big-12) +21.8 Fiesta (W 31-21 vs. Tennessee)
2007 West Virginia (big-east) +21.7 Fiesta (W 48-28 vs. Oklahoma)
2001 Nebraska (big-12) +21.7 Rose (L 14-37 vs. Miami (FL))
1999 Florida State (acc) +21.6 Sugar (W 46-29 vs. Virginia Tech)
2007 Ohio State (big-ten) +21.3 BCSCG (L 24-38 vs. Louisiana State)
2003 Michigan (big-ten) +21.3 Rose (L 14-28 vs. Southern California)
1998 Wisconsin (big-ten) +21.1 Rose (W 38-31 vs. UCLA)
2002 Iowa (big-ten) +20.8 Orange (L 17-38 vs. Southern California)
2009 Alabama (sec) +20.7 BCSCG (W 37-21 vs. Texas)
2006 Louisiana State (sec) +20.6 Sugar (W 41-14 vs. Notre Dame)
1998 Florida State (acc) +20.6 Fiesta (L 16-23 vs. Tennessee)
2008 Utah (mwc) +20.2 Sugar (W 31-17 vs. Alabama)
2004 Virginia Tech (acc) +19.7 Sugar (L 13-16 vs. Auburn)
2012 Kansas State (big-12) +19.6 Fiesta (L 17-35 vs. Oregon)
1998 Tennessee (sec) +19.6 Fiesta (W 23-16 vs. Florida State)
2009 Cincinnati (big-east) +19.1 Sugar (L 24-51 vs. Florida)
2013 Clemson (acc) +19.1 Orange (vs. Ohio State)
2007 Louisiana State (sec) +19.1 BCSCG (W 38-24 vs. Ohio State)
2004 Texas (big-12) +18.8 Rose (W 38-37 vs. Michigan)
2005 Penn State (big-ten) +18.7 Orange (W 26-23 vs. Florida State)
2010 Auburn (sec) +18.2 BCSCG (W 22-19 vs. Oregon)
2008 Alabama (sec) +18.2 Sugar (L 17-31 vs. Utah)
1998 Syracuse (big-east) +18.1 Orange (L 10-31 vs. Florida)
2005 Ohio State (big-ten) +17.7 Fiesta (W 34-20 vs. Notre Dame)
2000 Florida (sec) +17.7 Sugar (L 20-37 vs. Miami (FL))
1998 Florida (sec) +17.6 Orange (W 31-10 vs. Syracuse)
2002 Georgia (sec) +17.3 Sugar (W 26-13 vs. Florida State)
2009 Ohio State (big-ten) +17.1 Rose (W 26-17 vs. Oregon)
2013 Michigan State (big-ten) +17.1 Rose (vs. Stanford)
2011 Michigan (big-ten) +17.0 Sugar (W 23-20 vs. Virginia Tech)
2003 Florida State (acc) +17.0 Orange (L 14-16 vs. Miami (FL))
2002 Southern California (pac-10) +17.0 Orange (W 38-17 vs. Iowa)
2002 Ohio State (big-ten) +16.9 Fiesta (W 31-24 vs. Miami (FL))
1999 Tennessee (sec) +16.8 Fiesta (L 21-31 vs. Nebraska)
2010 Virginia Tech (acc) +16.5 Orange (L 12-40 vs. Stanford)
2012 Notre Dame (independent) +16.4 BCSCG (L 14-42 vs. Alabama)
2001 Maryland (acc) +16.4 Orange (L 23-56 vs. Florida)
2013 Auburn (sec) +16.2 BCSCG (vs. Florida State)
2006 Michigan (big-ten) +15.6 Rose (L 18-32 vs. Southern California)
2006 Southern California (pac-10) +15.4 Rose (W 32-18 vs. Michigan)
2006 Florida (sec) +15.4 BCSCG (W 41-14 vs. Ohio State)
2007 Southern California (pac-10) +15.3 Rose (W 49-17 vs. Illinois)
2005 West Virginia (big-east) +15.3 Sugar (W 38-35 vs. Georgia)
2008 Ohio State (big-ten) +15.1 Fiesta (L 21-24 vs. Texas)
2013 Stanford (pac-12) +14.6 Rose (vs. Michigan State)
2010 Arkansas (sec) +14.6 Sugar (L 26-31 vs. Ohio State)
2005 Notre Dame (independent) +14.5 Fiesta (L 20-34 vs. Ohio State)
2005 Georgia (sec) +14.5 Sugar (L 35-38 vs. West Virginia)
2010 Oklahoma (big-12) +14.5 Fiesta (W 48-20 vs. Connecticut)
2000 Oregon State (pac-10) +14.2 Fiesta (W 41-9 vs. Notre Dame)
2009 Oregon (pac-10) +14.1 Rose (L 17-26 vs. Ohio State)
2006 Oklahoma (big-12) +14.1 Fiesta (L 42-43 vs. Boise State)
2007 Virginia Tech (acc) +13.8 Orange (L 21-24 vs. Kansas)
2012 Florida (sec) +13.8 Sugar (L 23-33 vs. Louisville)
2003 Miami (FL) (big-east) +13.6 Orange (W 16-14 vs. Florida State)
2013 Central Florida (american) +13.6 Fiesta (vs. Baylor)
1998 UCLA (pac-10) +13.0 Rose (L 31-38 vs. Wisconsin)
2002 Washington State (pac-10) +12.9 Rose (L 14-34 vs. Oklahoma)
2001 Oregon (pac-10) +12.2 Fiesta (W 38-16 vs. Colorado)
2012 Wisconsin (big-ten) +11.7 Rose (L 14-20 vs. Stanford)
2000 Purdue (big-ten) +11.4 Rose (L 24-34 vs. Washington)
2011 Virginia Tech (acc) +11.3 Sugar (L 20-23 vs. Michigan)
2012 Stanford (pac-12) +11.0 Rose (W 20-14 vs. Wisconsin)
2007 Georgia (sec) +10.9 Sugar (W 41-10 vs. Hawaii)
2002 Florida State (acc) +10.8 Sugar (L 13-26 vs. Georgia)
2001 Illinois (big-ten) +10.7 Sugar (L 34-47 vs. Louisiana State)
2000 Notre Dame (independent) +10.7 Fiesta (L 9-41 vs. Oregon State)
2013 Oklahoma (big-12) +10.5 Sugar (vs. Alabama)
2009 Georgia Tech (acc) +10.5 Orange (L 14-24 vs. Iowa)
1999 Michigan (big-ten) +10.3 Orange (W 35-34 vs. Alabama)
1998 Texas A&M (big-12) +10.1 Sugar (L 14-24 vs. Ohio State)
2006 Notre Dame (independent) +10.0 Sugar (L 14-41 vs. Louisiana State)
2000 Washington (pac-10) +9.7 Rose (W 34-24 vs. Purdue)
2001 Colorado (big-12) +9.7 Fiesta (L 16-38 vs. Oregon)
1999 Alabama (sec) +9.5 Orange (L 34-35 vs. Michigan)
2007 Illinois (big-ten) +9.3 Rose (L 17-49 vs. Southern California)
2011 West Virginia (big-east) +8.7 Orange (W 70-33 vs. Clemson)
2001 Louisiana State (sec) +8.6 Sugar (W 47-34 vs. Illinois)
2004 Michigan (big-ten) +8.4 Rose (L 37-38 vs. Texas)
2005 Florida State (acc) +7.8 Orange (L 23-26 vs. Penn State)
2009 Iowa (big-ten) +7.6 Orange (W 24-14 vs. Georgia Tech)
2006 Wake Forest (acc) +7.5 Orange (L 13-24 vs. Louisville)
2011 Clemson (acc) +7.5 Orange (L 33-70 vs. West Virginia)
2012 Louisville (big-east) +7.2 Sugar (W 33-23 vs. Florida)
2003 Ohio State (big-ten) +7.2 Fiesta (W 35-28 vs. Kansas State)
2008 Cincinnati (big-east) +7.2 Orange (L 7-20 vs. Virginia Tech)
2010 Connecticut (big-east) +7.1 Fiesta (L 20-48 vs. Oklahoma)
2004 Pittsburgh (big-east) +5.9 Fiesta (L 7-35 vs. Utah)
1999 Stanford (pac-10) +5.6 Rose (L 9-17 vs. Wisconsin)
2008 Virginia Tech (acc) +4.8 Orange (W 20-7 vs. Cincinnati)

6 Comments | Posted in Advanced Stats, Announcement, CFB at Sports Reference, History, Pro-Football-Reference.com, SRS, Stat Questions, Statgeekery, Trivia

Speaking our Piece About Jack Morris

Posted by admin on December 5, 2013

I don't think it's a secret that the sabermetric case for Jack Morris is an especially thin one.  A ranking using WAR has him about the 25th best player on the ballot.  But we hear all of these stats about how much of a workhorse Morris was.  Here is an example from Tom Verducci.  Now it's true that Morris pitched into the 8th the most of his era, but when he did he was actually way below average among of group of pitchers who pitched 100+ outings of that length.

Morris worked deep in the games, but it was largely due to usage rather than effectiveness.   When he went 8 innings he was league average, when he went five innings he was league average.  The chart below shows the number of innings completed by the starter per start.  So the "0" row is not all first innings, but just the games they didn't make it out of the first inning.  Their complete games would be in the 9 row.  Now there is a value to pitching late into games and Morris should be credited by that value, but it certainly looks to me that a big reason Morris went late into games was the astronomical run support he was getting not because he was pitching so much better than the average pitcher.  Note that for outings last one inning or longer Morris' RA is WORSE than league average for every single outing length.

All AL SP's 1975-1997 Jack Morris Frank Tanana (1975 on)
completed innings % of all GS W-L% RA tmAvgRS % of all GS W-L% RA tmAvgRS % of all GS W-L% RA tmAvgRS
0 1.3 0.000 89.23 4.86 0.6 0.000 64.80 5.67 1.5 0.000 83.45 5.38
1 2.7 0.000 29.29 4.61 1.1 0.000 33.48 4.33 2.0 0.000 31.30 4.73
2 4.2 0.000 18.00 4.62 2.5 0.000 20.90 4.00 3.6 0.000 19.13 4.10
3 5.9 0.000 13.06 4.60 3.2 0.000 13.81 5.24 3.6 0.000 13.23 4.60
4 8.0 0.001 10.00 4.52 6.3 0.000 12.39 4.58 5.1 0.000 9.80 4.61
5 14.0 0.412 6.45 4.94 5.7 0.375 8.79 4.50 9.9 0.324 6.91 4.94
6 18.4 0.504 4.78 4.62 12.3 0.540 5.78 5.80 17.2 0.500 4.95 4.78
7 18.4 0.637 3.43 4.51 21.3 0.730 3.94 5.88 21.9 0.633 3.51 4.70
8 12.3 0.545 2.93 3.75 19.5 0.413 3.86 3.83 15.1 0.368 3.00 2.86
9 14.1 0.842 1.68 4.91 26.0 0.867 1.77 4.53 18.8 0.880 1.48 4.97
10 0.5 0.713 1.88 2.76 1.3 0.250 2.44 2.14 0.4 0.000 0.90 0.50
11 0.1 0.667 1.63 2.54 0.2 1.64 3.00 0.4 1.000 0.82 2.50
12 0.0 0.667 1.59 2.75 0.2 3.00 5.00
13 0.0 0.59 2.57 0.4 0.00 1.50
14 0.0 0.750 1.77 4.25

 

It seems to me if the basis of your argument for Morris in the HOF was that he pitched deep into a lot of games (and was about avg in those outings) then you have a pretty weak argument.  The summary of our view is that Morris was a pretty good pitcher on very good teams, but really is not a whole lot better than someone like David Wells or Frank Tanana.  And certainly not better than Mike Mussina or Kevin Brown.

95 Comments | Posted in Announcement, Baseball-Reference.com, Hall of Fame, Stat Questions, Statgeekery

NFL Team Records vs. Each Division Since 2002

Posted by Neil on October 24, 2013

A user wrote in asking how many games each team has played vs. every division since the 2002 expansion set up the NFL's current 8-division alignment. Since we don't currently have that function in the Team Game Finder (though we might someday), I figured I'd just run a quick database search on the subject...

First, records vs. the AFC divisions:

+---------+-----------+--------------+--------------+--------------+--------------+
| team_id | division  |   AFC East   |  AFC North   |  AFC South   |   AFC West   |
+---------+-----------+--------------+--------------+--------------+--------------+
| atl     | NFC South | 11 (6-5-0)   | 12 (7-4-1)   | 12 (5-7-0)   | 12 (10-2-0)  |
| buf     | AFC East  | 69 (23-46-0) | 23 (11-12-0) | 23 (8-15-0)  | 23 (11-12-0) |
| car     | NFC South |  9 (3-6-0)   | 12 (5-7-0)   | 12 (5-7-0)   | 12 (7-5-0)   |
| chi     | NFC North | 12 (6-6-0)   | 10 (5-5-0)   | 12 (6-6-0)   | 12 (7-5-0)   |
| cin     | AFC North | 22 (6-16-0)  | 68 (32-36-0) | 23 (10-13-0) | 23 (11-12-0) |
| cle     | AFC North | 22 (12-10-0) | 68 (17-51-0) | 23 (10-13-0) | 23 (9-14-0)  |
| clt     | AFC South | 24 (13-11-0) | 23 (18-5-0)  | 67 (49-18-0) | 23 (15-8-0)  |
| crd     | NFC West  | 12 (5-7-0)   | 12 (5-7-0)   |  8 (3-5-0)   | 12 (2-10-0)  |
| dal     | NFC East  | 12 (7-5-0)   | 12 (6-6-0)   | 12 (7-5-0)   | 11 (4-7-0)   |
| den     | AFC West  | 23 (12-11-0) | 24 (18-6-0)  | 22 (9-13-0)  | 67 (39-28-0) |
| det     | NFC North | 12 (2-10-0)  | 10 (4-6-0)   | 12 (2-10-0)  | 12 (8-4-0)   |
| gnb     | NFC North | 12 (6-6-0)   | 11 (4-7-0)   | 12 (5-7-0)   | 12 (10-2-0)  |
| htx     | AFC South | 23 (12-11-0) | 24 (10-14-0) | 67 (26-41-0) | 22 (11-11-0) |
| jax     | AFC South | 23 (9-14-0)  | 23 (9-14-0)  | 67 (26-41-0) | 24 (13-11-0) |
| kan     | AFC West  | 23 (8-15-0)  | 23 (9-14-0)  | 23 (11-12-0) | 67 (30-37-0) |
| mia     | AFC East  | 67 (26-41-0) | 22 (7-15-0)  | 24 (8-16-0)  | 23 (16-7-0)  |
| min     | NFC North | 12 (2-10-0)  | 10 (5-5-0)   | 12 (8-4-0)   | 12 (4-8-0)   |
| nor     | NFC South | 10 (7-3-0)   | 12 (5-7-0)   | 12 (6-6-0)   | 12 (7-5-0)   |
| nwe     | AFC East  | 69 (55-14-0) | 21 (15-6-0)  | 23 (17-6-0)  | 23 (14-9-0)  |
| nyg     | NFC East  | 12 (8-4-0)   | 12 (5-7-0)   | 12 (5-7-0)   | 10 (5-5-0)   |
| nyj     | AFC East  | 69 (33-36-0) | 21 (9-12-0)  | 24 (14-10-0) | 23 (12-11-0) |
| oti     | AFC South | 24 (13-11-0) | 24 (13-11-0) | 67 (33-34-0) | 22 (7-15-0)  |
| phi     | NFC East  | 12 (8-4-0)   | 12 (6-5-1)   | 12 (5-7-0)   | 11 (5-6-0)   |
| pit     | AFC North | 21 (14-7-0)  | 68 (47-21-0) | 24 (12-12-0) | 23 (11-12-0) |
| rai     | AFC West  | 23 (8-15-0)  | 23 (9-14-0)  | 22 (7-15-0)  | 69 (24-45-0) |
| ram     | NFC West  | 12 (2-10-0)  | 12 (5-7-0)   | 10 (5-5-0)   | 12 (6-6-0)   |
| rav     | AFC North | 22 (13-9-0)  | 68 (40-28-0) | 24 (12-12-0) | 24 (15-9-0)  |
| sdg     | AFC West  | 23 (11-12-0) | 23 (11-12-0) | 24 (18-6-0)  | 67 (42-25-0) |
| sea     | NFC West  | 12 (5-7-0)   | 12 (5-7-0)   | 12 (7-5-0)   | 12 (5-7-0)   |
| sfo     | NFC West  | 12 (6-6-0)   | 12 (5-7-0)   | 11 (4-7-0)   | 12 (6-6-0)   |
| tam     | NFC South | 10 (2-8-0)   | 12 (7-5-0)   | 12 (3-9-0)   | 12 (5-7-0)   |
| was     | NFC East  | 12 (4-8-0)   | 12 (3-9-0)   | 12 (7-5-0)   |  9 (3-6-0)   |
+---------+-----------+--------------+--------------+--------------+--------------+

And the NFC divisions:

+---------+-----------+--------------+--------------+--------------+--------------+
| team_id | division  |   NFC East   |  NFC North   |  NFC South   |   NFC West   |
+---------+-----------+--------------+--------------+--------------+--------------+
| atl     | NFC South | 23 (12-11-0) | 23 (13-10-0) | 68 (34-34-0) | 21 (15-6-0)  |
| buf     | AFC East  | 12 (5-7-0)   | 12 (6-6-0)   | 9 (3-6-0)    | 12 (7-5-0)   |
| car     | NFC South | 24 (8-16-0)  | 24 (12-12-0) | 66 (34-32-0) | 23 (15-8-0)  |
| chi     | NFC North | 22 (10-12-0) | 68 (37-31-0) | 24 (13-11-0) | 23 (12-11-0) |
| cin     | AFC North | 12 (8-3-1)   | 11 (9-2-0)   | 12 (4-8-0)   | 12 (6-6-0)   |
| cle     | AFC North | 12 (2-10-0)  | 11 (3-8-0)   | 12 (5-7-0)   | 12 (6-6-0)   |
| clt     | AFC South | 12 (7-5-0)   | 12 (9-3-0)   | 12 (7-5-0)   | 10 (9-1-0)   |
| crd     | NFC West  | 23 (10-13-0) | 24 (10-14-0) | 23 (8-15-0)  | 69 (30-39-0) |
| dal     | NFC East  | 69 (35-34-0) | 20 (10-10-0) | 23 (15-8-0)  | 24 (14-10-0) |
| den     | AFC West  | 11 (8-3-0)   | 12 (4-8-0)   | 12 (9-3-0)   | 12 (6-6-0)   |
| det     | NFC North | 21 (8-13-0)  | 69 (16-53-0) | 23 (8-15-0)  | 24 (7-17-0)  |
| gnb     | NFC North | 21 (12-9-0)  | 67 (48-19-0) | 23 (12-11-0) | 24 (17-7-0)  |
| htx     | AFC South | 12 (3-9-0)   | 12 (6-6-0)   | 12 (7-5-0)   | 11 (4-7-0)   |
| jax     | AFC South | 12 (6-6-0)   | 12 (5-7-0)   | 12 (6-6-0)   | 10 (4-6-0)   |
| kan     | AFC West  | 11 (5-6-0)   | 12 (7-5-0)   | 12 (3-9-0)   | 12 (10-2-0)  |
| mia     | AFC East  | 12 (4-8-0)   | 12 (7-5-0)   | 10 (5-5-0)   | 12 (7-5-0)   |
| min     | NFC North | 21 (10-11-0) | 68 (35-33-0) | 24 (9-15-0)  | 23 (15-8-0)  |
| nor     | NFC South | 23 (15-8-0)  | 24 (12-12-0) | 68 (37-31-0) | 21 (13-8-0)  |
| nwe     | AFC East  | 12 (10-2-0)  | 12 (11-1-0)  | 11 (9-2-0)   | 12 (9-3-0)   |
| nyg     | NFC East  | 68 (37-31-0) | 22 (11-11-0) | 24 (12-12-0) | 23 (15-8-0)  |
| nyj     | AFC East  | 12 (2-10-0)  | 12 (8-4-0)   | 10 (5-5-0)   | 12 (7-5-0)   |
| oti     | AFC South | 12 (8-4-0)   | 12 (7-5-0)   | 12 (9-3-0)   | 10 (5-5-0)   |
| phi     | NFC East  | 69 (41-28-0) | 20 (12-8-0)  | 24 (15-9-0)  | 23 (14-9-0)  |
| pit     | AFC North | 12 (9-3-0)   | 10 (7-3-0)   | 12 (8-3-1)   | 12 (7-5-0)   |
| rai     | AFC West  | 9 (3-6-0)    | 12 (3-9-0)   | 12 (3-9-0)   | 12 (5-7-0)   |
| ram     | NFC West  | 24 (7-17-0)  | 23 (8-15-0)  | 22 (8-14-0)  | 68 (25-42-1) |
| rav     | AFC North | 12 (8-4-0)   | 9 (4-5-0)    | 12 (6-6-0)   | 12 (9-3-0)   |
| sdg     | AFC West  | 10 (8-2-0)   | 12 (5-7-0)   | 12 (4-8-0)   | 12 (8-4-0)   |
| sea     | NFC West  | 23 (9-14-0)  | 23 (13-10-0) | 21 (12-9-0)  | 68 (42-26-0) |
| sfo     | NFC West  | 23 (8-15-0)  | 24 (12-12-0) | 20 (5-15-0)  | 69 (39-29-1) |
| tam     | NFC South | 24 (9-15-0)  | 23 (15-8-0)  | 68 (30-38-0) | 21 (10-11-0) |
| was     | NFC East  | 68 (24-44-0) | 23 (12-11-0) | 23 (8-15-0)  | 23 (16-7-0)  |
+---------+-----------+--------------+--------------+--------------+--------------+

Comments Off on NFL Team Records vs. Each Division Since 2002 | Posted in Announcement, Data, Pro-Football-Reference.com, Stat Questions, Trivia

Stat Questions: What’s Up With Those “vs Starter” Platoon Splits?

Posted by Neil on October 18, 2013

Here's another question I get pretty regularly when answering our bug reports: what's the deal with those "vs LH/RH Starter" platoon splits? To wit:

"How can Reed Johnson hit 32 HR's off of LH Starters when he hit 27 vs LHP?"

At a glance, it seems like we're saying "this is the player's stat line just against the opposing starter, broken down by that starter's handedness." In which case it would seem to be a bug when a guy has bigger totals against, say, lefty starters than against lefties in total.

Except that the definition I just wrote is not at all what that split is measuring. Instead, the "vs LH/RH Starter" split adds up all stats accumulated in games where the opposing starter was of a certain handedness, INCLUDING STATS ACCUMULATED LATER IN THE GAME WHEN THE STARTER IS PULLED, REGARDLESS OF THE RELIEF PITCHER(S)' HANDEDNESS.

Despite bolding, italicizing, and going all-caps, I still don't think I emphasized that enough. I realize the description of the split seems like it's talking only about stats accumulated against the starters, but it's really just counting up all stats in games where the opposing starter threw a certain way -- a BIG difference. If you want to know about performance against just starters of a given handedness... well, that's a double split, so we can't answer that right now. But we do hope to add the capacity for double splits in the future.

Anyway, now you might be saying, OK, well what's the point of that split, then? It seems pretty useless. And in some ways it is, for recent seasons at least -- but it's the only way we can approximate platoon splits for pre-PBP era players (1940s and before). Even going back to 1916, we at least know who started each game and their handedness, which usually is good enough to get platoon data on the majority of PAs (especially before managers started aggressively using relievers).

So that's why that split is there, even if it's not especially useful for 2013 players. And now you know what that split means, even if it seems like it should mean that other thing (which it DOESN'T).

3 Comments | Posted in Announcement, Baseball-Reference.com, Stat Questions

Stat Questions: Team Passing Yards vs Individual Passing Yards

Posted by Neil on October 16, 2013

This is a question we get ALL the time... In fact, it's come up twice in my inbox over the last 2 days:

"How is a total of -11 (-1 Dallas and -10 Green Bay) passing yards figured in the Oct 24, 1965 game when Individual stats show Craig Morton for 61 yards and Bart Starr for 42 yards?"

"This query should return this game [...] Elvis Grbac threw for 504 yards, and the Raiders scored 49 points. That game was not returned by the query[.]"

In both cases, it's a question of individual passing totals seemingly not matching up to team totals. And that's because the NFL doesn't define "passing yards" in the same way for teams as it does for individuals.

Confusingly, sack yards do not count against the "passing yards" you see listed next to individual players, but they do count against "passing yards" at the team level. To put it another way, individual passing yards are always presented as GROSS yards (with sack yards not subtracted out), while team passing yards are always presented as NET yards (meaning sack yards are subtracted out).

So despite Morton & Starr's combined GROSS pass yards, there were also 114 combined sack yards in that game, leading to a record -11 total combined NET passing yards in the game. And despite Grbac's 504 GROSS pass yards, he was sacked 4 times for 30 yards, meaning KC only had 474 NET passing yards in the game.

This probably doesn't make a great deal of sense in 2013, but it does come in handy for years before QB sack data was tracked (a.k.a. the history of football up until 1969). Because of this practice, we know how many gross yards a quarterback -- and, therefore, a team -- had, as well as how many net yards they had, since sack totals were recorded for teams (but not players) before 1969. It would make for even more confusion if, starting in 1969, we all of a sudden began defining yardage for individuals differently than it had been defined in the past (though as we saw yesterday, this didn't stop the NBA from abruptly changing how team rebound totals were defined).

So it's a minor inconvenience now, but probably a necessary one from an historical perspective. And hopefully this post will reduce some of the confusion going forward (though I doubt it).

Comments Off on Stat Questions: Team Passing Yards vs Individual Passing Yards | Posted in Announcement, Pro-Football-Reference.com, Stat Questions, Trivia

Stat Questions: Why Do Pre-1970s Team Rebound Totals Not Match the Sums of Individual Players?

Posted by Neil on October 15, 2013

Recently, a BBR user posed a question we get on occasion:

"[I] noticed that the team rebound totals for every team appear to be incorrect from 1956-57 to 1967-68. They are correct in 1968-69. For example in 1956-57 it shows the Celtics led the league in rebounds with 4963. When you add up the rebounds for every player on their roster the total comes to 4578. The points and assists totals match exactly. [...] It is only the rebound total that is off, and it is off for every team by a significant margin."

He's right -- you can see this by adding up the team rebound totals for each league-season and comparing it to the sum of individual player rebounds... It changes around 1968:

+---------+-------+----------+------------+
| year_id | lg_id | team_trb | player_trb |
+---------+-------+----------+------------+
|    2013 | NBA   |   103575 |     103575 |
|    2012 | NBA   |    83513 |      83513 |
|    2011 | NBA   |   101816 |     101816 |
|    2010 | NBA   |   102640 |     102640 |
|    2009 | NBA   |   101586 |     101586 |
|    2008 | NBA   |   103271 |     103271 |
|    2007 | NBA   |   100994 |     100994 |
|    2006 | NBA   |   100754 |     100754 |
|    2005 | NBA   |   102970 |     102970 |
|    2004 | NBA   |   100361 |     100361 |
|    2003 | NBA   |   100604 |     100604 |
|    2002 | NBA   |   100829 |     100829 |
|    2001 | NBA   |   100988 |     100988 |
|    2000 | NBA   |   102062 |     102062 |
|    1999 | NBA   |    60395 |      60395 |
|    1998 | NBA   |    98798 |      98798 |
|    1997 | NBA   |    97703 |      97703 |
|    1996 | NBA   |    98099 |      98099 |
|    1995 | NBA   |    92006 |      92006 |
|    1994 | NBA   |    95192 |      95192 |
|    1993 | NBA   |    95504 |      95504 |
|    1992 | NBA   |    96680 |      96680 |
|    1991 | NBA   |    95776 |      95776 |
|    1990 | NBA   |    95518 |      95518 |
|    1989 | NBA   |    90031 |      90031 |
|    1988 | NBA   |    81828 |      81828 |
|    1987 | NBA   |    83020 |      83020 |
|    1986 | NBA   |    82161 |      82161 |
|    1985 | NBA   |    82008 |      82008 |
|    1984 | NBA   |    81150 |      81150 |
|    1983 | NBA   |    83853 |      83853 |
|    1982 | NBA   |    81987 |      81987 |
|    1981 | NBA   |    82010 |      82010 |
|    1980 | NBA   |    81065 |      81065 |
|    1979 | NBA   |    81564 |      81564 |
|    1978 | NBA   |    84984 |      84984 |
|    1977 | NBA   |    84886 |      84886 |
|    1976 | NBA   |    69960 |      69960 |
|    1976 | ABA   |    30815 |      30815 |
|    1975 | NBA   |    69468 |      69468 |
|    1975 | ABA   |    40239 |      40239 |
|    1974 | NBA   |    67230 |      67230 |
|    1974 | ABA   |    40705 |      40705 |
|    1973 | NBA   |    70555 |      70555 |
|    1973 | ABA   |    41062 |      41062 |
|    1972 | NBA   |    71299 |      71299 |
|    1972 | ABA   |    48429 |      48429 |
|    1971 | NBA   |    74059 |      74059 |
|    1971 | ABA   |    50234 |      50234 |
|    1970 | NBA   |    60697 |      60697 |
|    1970 | ABA   |    49991 |      49991 |
|    1969 | NBA   |    65324 |      65324 |
|    1969 | ABA   |    46674 |      46674 |
|    1968 | NBA   |    65166 |      56999 |
|    1968 | ABA   |    46957 |      46957 |
|    1967 | NBA   |    54536 |      48095 |
|    1966 | NBA   |    49118 |      43250 |
|    1965 | NBA   |    48433 |      42563 |
|    1964 | NBA   |    47423 |      41894 |
|    1963 | NBA   |    48044 |      41418 |
|    1962 | NBA   |    51415 |      43183 |
|    1961 | NBA   |    46314 |      40732 |
|    1960 | NBA   |    44104 |      37401 |
|    1959 | NBA   |    40343 |      34794 |
|    1958 | NBA   |    41279 |      35814 |
|    1957 | NBA   |    35948 |      32136 |
|    1956 | NBA   |    34616 |      31395 |
|    1955 | NBA   |    32292 |      28777 |
|    1954 | NBA   |    32987 |      29808 |
|    1953 | NBA   |    36168 |      32314 |
|    1952 | NBA   |    35977 |      31209 |
|    1951 | NBA   |    35019 |      30621 |
+---------+-------+----------+------------+

So what's the deal? Is this a bug in our database?

Actually, no. It's just a matter of accounting by the official scorer, because technically speaking, for every missed shot there has to be a rebound.

Starting in 1968-69 (1967-68 in the ABA), so-called "team" rebounds (rebounds where there was no clear individual who should receive credit) were no longer counted toward a team's overall rebounding total. Before that, "team rebounds" were credited at the team level, but they (obviously) didn't make it into individual rebounding totals. This is what caused the massive discrepancies between teams' rebounds and the sum of their individual players' rebounds.

Nowadays, those "team rebounds" are thrown into their own bucket, neither allocated to individuals nor to teams for the purpose of overall stat totals.

Mystery solved!

1 Comment | Posted in Announcement, Basketball-Reference.com, Stat Questions, Trivia

Grand Slams with 4 Pitchers Receiving Earned Runs

Posted by admin on October 14, 2013

Last night's Ortiz Grand Slam was the first in the postseason and 8th time in the RetroSheet canon (but probably overall) that four pitchers received earned runs on one play.

| game_id      | result_batter | batting_team_id | pitching_team_id | event_text           | home      | first     | second    | third     |
+--------------+---------------+-----------------+------------------+----------------------+-----------+-----------+-----------+-----------+
| BOS201310130 | ortizda01     | BOS             | DET              | HR/9/L9D.3-H;2-H;1-H | benoijo01 | albural01 | smylydr01 | verasjo01 |
+--------------+---------------+-----------------+------------------+----------------------+-----------+-----------+-----------+-----------+
+--------------+---------------+-----------------+------------------+-------------------------+-----------+-----------+-----------+-----------+
| game_id      | result_batter | batting_team_id | pitching_team_id | event_text              | home      | first     | second    | third     |
+--------------+---------------+-----------------+------------------+-------------------------+-----------+-----------+-----------+-----------+
| MIN196107041 | becquju01     | MIN             | CHW              | HR/9D.3-H;2-H;1-H       | hackewa02 | baumafr01 | kemmeru01 | piercbi02 |
| SEA198208060 | jacksre01     | CAL             | SEA              | HR/9.3-H;2-H;1-H        | vandeed01 | stantmi01 | anderla02 | bannifl01 |
| DET198307100 | parrila02     | DET             | OAK              | HR/7.3-H(UR);2-H;1-H    | jonesje01 | beardda01 | burgmto01 | codirch01 |
| SFN198709200 | esaskni01     | CIN             | SFG              | HR/7D.3-H;2-H;1-H       | perlmjo01 | leffecr01 | downske01 | robindo01 |
| SEA198805060 | sheripa01     | DET             | SEA              | HR/9.3-H;2-H;1-H        | jacksmi02 | solanju01 | powelde01 | mooremi01 |
| SDN200109010 | lankfra01     | SDP             | ARI              | HR/89.3-H;2-H;1-H       | kimby01   | sabeler01 | knotter01 | batismi01 |
| LAN200410020 | finlest01     | LAD             | SFG              | HR/89/F.3-H;2-H(UR);1-H | frankwa01 | hergema01 | chrisja01 | hermadu01 |

6 Comments | Posted in Advanced Stats, Announcement, Baseball-Reference.com, History, Stat Questions, Statgeekery, Trivia

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