Tag Archives: risp

2014 Event Files have arrived

From http://retrosheet.org/

What’s New

  • 12/14/2014: Game accounts, boxes, and play-by-play data files for 2014; many other updates and additions. See [Games/Regular Season]

The 2014 event files have arrived at retrosheet.org.  I need to process them to add game state information. After the files have been processed they go through a second set of scripts to generate run data and error data.  Run data is a set of records, one for every run, that marks batter who scored, batter who made RBI, pitcher credited for that run, type of play that caused that run, etc.  Error records identify the fielder credited with that error and how many runs resulted in that error along with other info.

Once all this data is generated the third set of scripts can count various things and not have to worry about figuring out game state or who hit in what run.  Calculations like RISP and GWRBI are two stats dependent on traversing game events.  Those stats will be coming soon as well as day by day graphs of player WAA for every player, every year since 1974, the earliest year game event data is considered complete.

Lineups and RISP opportunities

In baseball RISP opportunities are plate appearances  when a batter is up with a runner in scoring position.  A runner in scoring position means there is a runner on either second or third that will usually score on any kind of hit.

The following tables show results of a calculation using event data from retrosheet.org.   This study counted  RISP opportunities for each position of a lineup. Event data are pretty much accurate from around 1972 to 2013 so I split that period into two to see  a difference between the two periods. The rates are mostly the same with the 4th and 5th lineup positions seeing the most RISP opportunities and #1 and #2 seeing the least in each period.  In general teams want to place high OBP players, players who can get on base and not necessarily hit in RISP situations to bat #1 and #2.  Batters who hit well in RISP situations should placed in the part of the lineup where they see the most opportunities.  More math is needed to create an optimum solution and it would depend upon the complete set of players a manger has to work with.

The most glaring difference between the two periods is lineup position #2 which seems to have seen a drop off in RISP opportunities in the last 20 years compared to the 20 years before that.

Counting RISP opportunities is only one of many kind of studies that can be done having complete event files.  Here is another analysis of RISP done by this data model.  Here is an explanation of event data.

1993-2013

Pos Total PA RISP PA % RISP
1 465283 138957 0.299
2 454419 129969 0.286
3 443644 142793 0.322
4 433643 162596 0.375
5 423491 150451 0.355
6 412837 135644 0.329
7 401673 133209 0.332
8 390281 130213 0.334
9 378588 122383 0.323

1972-1992

POS Total PA RISP PA % RISP
1 397431 117386 0.295
2 388189 113635 0.293
3 379108 125617 0.331
4 370463 140369 0.379
5 361915 126758 0.350
6 352682 114248 0.324
7 343007 113034 0.330
8 332908 111013 0.333
9 322685 102891 0.319

RISP Averages

The table below runs through the last 24 years to calculate 1) what percentage of runs are scored in situations with runners in scoring position and 2) what is the percentage of plate appearances that result in runners in scoring position? According to the table below and after tabulating all 24 years of data the two RISP averages are;  #1 is almost exactly 3/4 and  #2 is almost exactly 1/3.

Year RISP Runs # Runs % RISP PAs # PAs %
1990 13859 17919 0.77 52681 160316 0.33
1991 13994 18128 0.77 52186 160746 0.32
1992 13534 17341 0.78 52807 160545 0.33
1993 16149 20862 0.77 58151 174564 0.33
1994 11866 15751 0.75 41791 124483 0.34
1995 14864 19554 0.76 52496 156703 0.34
1996 17165 22832 0.75 59086 177261 0.33
1997 16256 21602 0.75 58218 175541 0.33
1998 17589 23296 0.76 62816 188280 0.33
1999 18498 24690 0.75 63963 189692 0.34
2000 18639 24969 0.75 63801 190261 0.34
2001 17007 23199 0.73 60788 186976 0.33
2002 16884 22408 0.75 61084 186615 0.33
2003 17200 22978 0.75 61237 187449 0.33
2004 17342 23376 0.74 61806 188539 0.33
2005 16711 22325 0.75 60363 186292 0.32
2006 17575 23599 0.74 61943 188071 0.33
2007 17732 23322 0.76 62470 188623 0.33
2008 16944 22585 0.75 61270 187631 0.33
2009 16763 22419 0.75 61202 187079 0.33
2010 16074 21308 0.75 60062 185553 0.32
2011 15564 20808 0.75 58949 185245 0.32
2012 15285 21016 0.73 57609 184179 0.31
2013 14874 20255 0.73 57110 184873 0.31
TOTAL 388368 516542 0.75 1403889 4285517 0.33

2013 RISP Results

The RISP stat as described in this data model measures clutch hitting by using more than simple batting average. Previously we introduced RISP with an explanation of how it was derived for the 2012 season using event data. Now that the 2013 event data corpus has been released we can now show 2013 RISP results, both the top and bottom ten.

Note: All stats below are with respect to plate appearances with runners in scoring postiions.  As described in the introduction to RISP the sum of all Runs Above Average (RAA) equates to zero. This list excluded all batters with less than 100 RISP PAs.

Top Ten 2013 RISP Results

Being able to knock in runs when runners are in scoring position is why Miguel Cabrera was the top MLB batter for the 2013 season.

Rk RAA BA OBP PA RBI HR Name_team
1 50.7 0.390 0.523 220 101 18 Miguel_Cabrera_DET
2 40.6 0.391 0.498 205 90 8 Freddie_Freeman_ATL
3 38.5 0.330 0.464 220 90 13 Paul_Goldschmidt_ARI
4 38.3 0.405 0.473 184 86 4 Allen_Craig_SLN
5 34.0 0.314 0.414 220 89 17 Chris_Davis_BAL
6 33.9 0.321 0.424 198 83 9 Robinson_Cano_NYA
7 29.3 0.289 0.364 217 86 9 Adrian_Gonzalez_LAN
8 27.1 0.364 0.467 167 68 5 Matt_Holliday_SLN
9 26.6 0.291 0.366 227 86 6 Brandon_Phillips_CIN
10 25.3 0.318 0.458 214 75 5 Mike_Trout_ANA

Bottom Ten 2013 RISP Results

Rk RAA BA OBP PA RBI HR Name_team
850 -13.0 0.238 0.322 146 25 3 Starling_Marte_PIT
851 -13.4 0.221 0.280 157 29 1 Eric_Young_NYN
852 -13.5 0.237 0.356 180 31 0 Chase_Headley_SDN
853 -13.9 0.243 0.293 150 27 2 Andy_Dirks_DET
854 -14.1 0.153 0.242 153 26 1 Mike_Moustakas_KCA
855 -15.5 0.149 0.283 120 14 3 Rickie_Weeks_MIL
856 -15.8 0.167 0.217 83 7 0 Juan_Pierre_MIA
857 -16.0 0.171 0.218 87 8 1 Clete_Thomas_MIN
858 -17.8 0.237 0.282 188 34 3 Starlin_Castro_CHN
859 -18.4 0.107 0.239 142 17 1 B.J._Upton_ATL
860 -18.7 0.198 0.254 201 36 1 Andrelton_Simmons_ATL

RISP Leaders in Post Season

Since MLB post season is occurring now I thought it would be interesting to show the RISP leaders in post season from 1903-2012.

Rank RAA BA OBP PA Pa(tot) RBI HR Name_Tm %RISP
1 18.9 0.373 0.553 94 321 27 7 Albert_Pujols_SLN 0.293
2 18.1 0.286 0.429 175 493 35 11 Manny_Ramirez_TBA 0.355
3 12.9 0.283 0.468 62 150 16 4 Lou_Gehrig_TOT 0.413
4 12.8 0.296 0.422 206 545 37 7 Bernie_Williams_NYA 0.378
5 12.2 0.315 0.462 93 224 20 5 Lance_Berkman_SLN 0.415
6 11.2 0.368 0.486 107 289 20 6 David_Ortiz_MIN 0.370
7 11.0 0.357 0.500 54 178 12 2 Miguel_Cabrera_TOT 0.303
8 10.7 0.300 0.426 61 198 11 2 Shane_Victorino_PHI 0.308
9 9.9 0.413 0.500 54 208 8 5 Davey_Lopes_TOT 0.260
10 9.5 0.306 0.412 85 263 13 2 Roberto_Alomar_TOT 0.323