Foundations of Database Handicapping

 

 

Benchmark Testing

My purpose in writing this Help Document is to help you understand how Data Window research can help you in your handicapping. Not all horses that enter a starting gate have an equal historical probability of winning the race. Some horses have advantages over today’s field. And others have disadvantages. Understanding these advantages and disadvantages, and using that understanding in your live play, is what database handicapping is all about.

 

I think looking at benchmark tests of JCapper factors helps new users to shorten the learning curve. If nothing else, benchmark tests serve to give both context and meaning to the factors found in the program. In this document I’m not going to examine every factor in the program – only some of them. But after you’ve built a few databases of your own using your Bris or TSN files you’ll certainly have the ability to use the Data Window to run your own tests on as many factors as you like.

 

As the author of a handicapping program like JCapper, I have found one caveat to be true about database handicapping. It is far easier to create profitable UDMs when using a good starting point (as opposed to using a bad one.) It should be obvious that good starting points in your handicapping relate to high win percentages and/or high roi. Horses with historically low win percentages and/or low roi usually make terrible starting points for UDMs.

 

After reading this document the value of database handicapping should start to become apparent to you.

 

The following benchmark tests were taken from actual Data Window queries of my own calendar year 2006 database. All data samples presented in this Help Doc are based on data found in Bris $1.00 Single Format DRF Data Files.

 

ALL Starters in the Database

I always think it’s a good idea to begin any discussion of historical thoroughbred racing data with a sample that includes all horses in the database. This gives you a point of reference when you begin your handicapping. If you are doing a good job in your handicapping, horses you select will substantially outperform horses selected at random. When you run a Data Window query using the ALL Button, the Data Window shows you a recap of all starters in the database. In JCapper, the ALL Button (all starters in the database) is the closest thing you have to horses selected at random. Using the ALL Button, here’s what my calendar year 2006 database looks like:

 

     Data Window Settings: (RUN 3/18/2007)

     999 Divisor

     Surface: (ALL*)  Distance: (All*)

     (From Index File: D:\2007\Q1_2007\pl_Complete_History_06.txt)

 

 

     Data Summary         Win     Place      Show

     Mutuel Totals  309882.70 305170.00 303211.00

     Bet           -404684.00-404684.00-404684.00

     Gain           -94801.30 -99514.00-101473.00

 

     Wins               24997     49798     73448

     Plays             202342    202342    202342

     PCT                .1235     .2461     .3630

 

     ROI               0.7657    0.7541    0.7493

     Avg Mut            12.40      6.13      4.13

 

 

 

Post Time Odds vs. JPRToteProb

 

Post Time Odds

It has been said that the betting public makes a pretty good opponent. No, I’m not talking about each individual bettor. I’m talking about the collective intelligence of all bettors everywhere. As each race is bet, tens of thousands of bettors each weigh and apply the dozen or so (in some cases more) handicapping factors they think will shape the outcome of the race at hand. Collectively, they reach, by my rough estimate, somewhere between one and two million decision points as they handicap each race. As long as there has been modern day thoroughbred racing, post time favorites have won approximately 33 percent of all races. However, in recent years, because average field size has been shrinking, the win percentage of post time favorites has gone up slightly higher. My calendar year 2006 database shows that post time favorites (with no attempt to break ties for post time favoritism) won almost 35 percent of all races. 81.47 percent of all races were won by one of the first four choices in the betting. That should tell you quite clearly that quite a bit of collective intelligence exists in the odds.

 

 

     By: Odds Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1   -8925.70  52064.00    0.8286   9103  26032   .3497     2.8306 

        2   -9449.50  49120.00    0.8076   5120  24560   .2085     1.6875 

        3   -9790.00  49288.00    0.8014   3705  24644   .1503     1.2170 

        4  -10366.90  49148.00    0.7891   2609  24574   .1062     0.8594 

        5  -10250.00  48728.00    0.7896   1865  24364   .0765     0.6196 

        6  -11820.00  45690.00    0.7413   1162  22845   .0509     0.4117 

        7  -10164.50  38204.00    0.7339    699  19102   .0366     0.2962 

        8   -8003.30  28648.00    0.7206    377  14324   .0263     0.2130 

        9   -4383.80  19734.00    0.7779    225   9867   .0228     0.1846 

       10   -6529.30  12770.00    0.4887     75   6385   .0117     0.0951 

       11   -2924.70   6686.00    0.5626     37   3343   .0111     0.0896 

       12   -1397.80   3636.00    0.6156     18   1818   .0099     0.0801 

       13    -564.00    640.00    0.1188      1    320   .0031     0.0253 

       14    -219.80    316.00    0.3044      1    158   .0063     0.0512 

       15      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       16      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       17      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       18      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       19      -4.00      4.00    0.0000      0      2   .0000     0.0000 

 

 

JPRToteProb

JPRToteProb is the result of sending JPR (JCapper Power Rating) and post time odds into an algorithm that calculates a probability after the odds are known. This algorithm combines the collective intelligence of the betting public with JPR and produces a very accurate probability.

 

How accurate is this probability?

 

My calendar year 2006 database shows that JPRToteProb is even more accurate than the probability inherent in the odds set by the betting public. The top three ranked JPRToteProb horses each won a higher percentage of their races than the top three horses ranked by post time odds. One of the top four JPRToteProb horses won 82.25 percent of all races in the database. Further, the top four ranked JPRToteProb horses had a higher flat bet win roi than the top four horses ranked by post time odds.

 

     By: JPRToteProb Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1   -7823.60  49682.00    0.8425   8755  24841   .3524     2.8529 

        2   -9145.30  49708.00    0.8160   5325  24854   .2143     1.7343 

        3   -9542.30  49636.00    0.8078   3735  24818   .1505     1.2182 

        4  -10626.60  49666.00    0.7860   2615  24833   .1053     0.8524 

        5  -10582.70  48998.00    0.7840   1877  24499   .0766     0.6202 

        6  -11837.80  45960.00    0.7424   1189  22980   .0517     0.4188 

        7   -9910.10  38426.00    0.7421    728  19213   .0379     0.3067 

        8   -6129.90  28704.00    0.7864    440  14352   .0307     0.2482 

        9   -7719.10  19816.00    0.6105    194   9908   .0196     0.1585 

       10   -5898.00  12806.00    0.5394     84   6403   .0131     0.1062 

       11   -3671.20   7076.00    0.4812     34   3538   .0096     0.0778 

       12   -1622.40   3388.00    0.5211     16   1694   .0094     0.0765 

       13    -156.50    586.00    0.7329      4    293   .0137     0.1105 

       14    -123.80    220.00    0.4373      1    110   .0091     0.0736 

       15      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       16      -4.00      4.00    0.0000      0      2   .0000     0.0000 

       17       0.00      0.00    0.0000      0      0   .0000     0.0000 

       18      -4.00      4.00    0.0000      0      2   .0000     0.0000 

       19      -2.00      2.00    0.0000      0      1   .0000     0.0000 

 

 

My calendar year 2006 database broken out by JPRToteProb numeric value is shown below. If nothing else this next chart should give you a clear sense of the algorithm’s accuracy. The probability range for each row is indicated by the values in the min and max columns. The actual win percentage along with number of wins, plays, and flat bet win roi is also shown. For example, the top row shows data for horses where the algorithm said the probability of winning the race was between 0 and 5 percent. The win rate achieved for the 62,157 horses in that row was actually 2.63 percent.

 

     By: JPRToteProb

 

     >=Min       <Max      Gain       Bet       Roi   Wins  Plays     Pct   Impact

   -999.00       0.05 -41396.00 124314.00    0.6670   1634  62157   .0263   0.2128

      0.05       0.10 -21179.40 100082.00    0.7884   3606  50041   .0721   0.5833

      0.10       0.15 -10819.40  54184.00    0.8003   3346  27092   .1235   0.9997

      0.15       0.20  -8354.80  45586.00    0.8167   3927  22793   .1723   1.3946

      0.20       0.25  -4676.10  26008.00    0.8202   2923  13004   .2248   1.8195

      0.25       0.30  -3284.80  18180.00    0.8193   2470   9090   .2717   2.1995

      0.30       0.35  -2538.80  15730.00    0.8386   2504   7865   .3184   2.5771

      0.35       0.40  -1054.10   8024.00    0.8686   1514   4012   .3774   3.0547

      0.40       0.45   -845.00   6616.00    0.8723   1460   3308   .4414   3.5726

      0.45       0.50   -471.80   4054.00    0.8836   1028   2027   .5072   4.1052

      0.50       0.55    -66.60    722.00    0.9078    208    361   .5762   4.6640

      0.55       0.60   -109.80   1104.00    0.9005    349    552   .6322   5.1178

      0.60       0.65     -4.80     78.00    0.9385     27     39   .6923   5.6040

      0.65       0.70      0.10      2.00    1.0500      1      1  1.0000   8.0947

      0.70       0.75      0.00      0.00    0.0000      0      0   .0000   0.0000

      0.75       0.80      0.00      0.00    0.0000      0      0   .0000   0.0000

      0.80       0.85      0.00      0.00    0.0000      0      0   .0000   0.0000

      0.85       0.90      0.00      0.00    0.0000      0      0   .0000   0.0000

      0.90  999999.00      0.00      0.00    0.0000      0      0   .0000   0.0000

 

 

 

 

Morning Line Odds vs. JPRMLProb

 

 

Morning Line Odds

The morning line odds are set by a track employee known as the morning line oddsmaker. The morning line is not necessarily designed as an attempt to pick the winner of each race. Instead, the job of the morning line oddsmaker is often twofold: First, the morning line can be a prediction of how the public will bet the race. And second, it can be an attempt by the morning line oddsmaker to maximize betting on the race for the track. Nonetheless, many morning line oddsmakers are bright talented handicappers. My calendar year 2006 database shows that there is quite a bit of inherent predictability in the morning line odds. Nationally, the morning line favorite won approximately 31 percent of all races.

 

     By: Morning Line Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1  -10705.00  51096.00    0.7905   7963  25548   .3117     2.5230 

        2   -9762.10  51474.00    0.8103   5140  25737   .1997     1.6166 

        3  -10119.20  54976.00    0.8159   4082  27488   .1485     1.2021 

        4  -13038.50  54920.00    0.7626   2933  27460   .1068     0.8646 

        5  -10115.10  50654.00    0.8003   2045  25327   .0807     0.6536 

        6  -11783.40  44698.00    0.7364   1247  22349   .0558     0.4517 

        7   -8563.30  37392.00    0.7710    828  18696   .0443     0.3585 

        8   -7812.10  26848.00    0.7090    435  13424   .0324     0.2623 

        9   -5815.80  16902.00    0.6559    203   8451   .0240     0.1944 

       10   -3507.60   9406.00    0.6271     84   4703   .0179     0.1446 

       11   -2728.60   4278.00    0.3622     25   2139   .0117     0.0946 

       12    -468.60   1658.00    0.7174     12    829   .0145     0.1172 

       13    -284.00    284.00    0.0000      0    142   .0000     0.0000 

       14     -86.00     86.00    0.0000      0     43   .0000     0.0000 

       15     -12.00     12.00    0.0000      0      6   .0000     0.0000 

       16       0.00      0.00    0.0000      0      0   .0000     0.0000 

       17       0.00      0.00    0.0000      0      0   .0000     0.0000 

       18       0.00      0.00    0.0000      0      0   .0000     0.0000 

       19       0.00      0.00    0.0000      0      0   .0000     0.0000 

 

 

 

JPRMLProb

JPRMLProb is the result of feeding JPR (JCapper Power Rating) and the Morning Line Odds into an algorithm that calculates a probability before the odds are known. This probability has proven itself to be accurate across large data samples as evidenced by the chart below. Note that the win percent parallels that of morning line odds rank – with one important difference: The flat bet win roi for the top four ranked JPRMLProb horses taken as a whole significantly outperforms the top four ranked Morning Line Odds horses.

 

     By: JPRMLProb Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1   -5510.00  49654.00    0.8890   7678  24827   .3093     2.5034 

        2   -6193.70  49664.00    0.8753   5000  24832   .2014     1.6299 

        3  -10333.50  49672.00    0.7920   3879  24836   .1562     1.2643 

        4  -10617.70  49582.00    0.7859   2949  24791   .1190     0.9629 

        5  -13282.40  48994.00    0.7289   2054  24497   .0838     0.6787 

        6  -10823.40  45982.00    0.7646   1504  22991   .0654     0.5295 

        7  -10031.00  38414.00    0.7389    933  19207   .0486     0.3932 

        8   -9408.70  28812.00    0.6734    520  14406   .0361     0.2922 

        9   -8910.30  19882.00    0.5518    247   9941   .0248     0.2011 

       10   -5007.70  12824.00    0.6095    141   6412   .0220     0.1780 

       11   -3255.70   7928.00    0.5893     66   3964   .0166     0.1348 

       12   -1516.10   2930.00    0.4826     21   1465   .0143     0.1160 

       13      96.90    338.00    1.2867      5    169   .0296     0.2395 

       14      -8.00      8.00    0.0000      0      4   .0000     0.0000 

       15       0.00      0.00    0.0000      0      0   .0000     0.0000 

       16       0.00      0.00    0.0000      0      0   .0000     0.0000 

       17       0.00      0.00    0.0000      0      0   .0000     0.0000 

       18       0.00      0.00    0.0000      0      0   .0000     0.0000 

       19       0.00      0.00    0.0000      0      0   .0000     0.0000 

 

 

My calendar year 2006 database broken out by JPRMLProb numeric value is shown below. Like the tote probability chart, this next chart should give you a clear sense of the JPRMLProb algorithm’s accuracy. The probability range for each row is indicated by the values in the min and max columns. The actual win percentage along with number of wins, plays, and flat bet win roi is also shown. For example, the top row shows data for horses where the algorithm said the probability of winning the race was between 0 and 5 percent. The win rate achieved for the 55,967 horses in that row was actually 3.46 percent.

 

     By: JPRMLProb

 

    >=Min       <Max      Gain       Bet       Roi   Wins  Plays     Pct   Impact

  -999.00       0.05 -38810.50 111934.00    0.6533   1936  55967   .0346   0.2800

     0.05       0.10 -22963.10  92706.00    0.7523   3497  46353   .0754   0.6107

     0.10       0.15 -14039.60  66112.00    0.7876   4064  33056   .1229   0.9952

     0.15       0.20 -11443.20  63086.00    0.8186   5347  31543   .1695   1.3722

     0.20       0.25  -2134.50  21634.00    0.9013   2437  10817   .2253   1.8237

     0.25       0.30  -2509.90  20606.00    0.8782   2693  10303   .2614   2.1158

     0.30       0.35  -1659.80  16286.00    0.8981   2525   8143   .3101   2.5100

     0.35       0.40   -737.10   7398.00    0.9004   1413   3699   .3820   3.0921

     0.40       0.45   -396.50   3308.00    0.8801    687   1654   .4154   3.3622

     0.45       0.50    -58.70   1272.00    0.9539    310    636   .4874   3.9455

     0.50       0.55    -39.50    284.00    0.8609     72    142   .5070   4.1043

     0.55       0.60    -10.20     54.00    0.8111     14     27   .5185   4.1972

     0.60       0.65      1.30      4.00    1.3250      2      2  1.0000   8.0947

     0.65       0.70      0.00      0.00    0.0000      0      0   .0000   0.0000

     0.70       0.75      0.00      0.00    0.0000      0      0   .0000   0.0000

     0.75       0.80      0.00      0.00    0.0000      0      0   .0000   0.0000

     0.80       0.85      0.00      0.00    0.0000      0      0   .0000   0.0000

     0.85       0.90      0.00      0.00    0.0000      0      0   .0000   0.0000

     0.90  999999.00      0.00      0.00    0.0000      0      0   .0000   0.0000

 

 

Bris Prime Power vs. QRating

 

Bris Prime Power

Much has been written about the Bris Prime Power Rating. It is a pretty good rating. For years it has been viewed as a sort of benchmark among software generated power ratings. In fact very few software generated comprehensive power ratings have been able to duplicate its win rate and roi. My calendar year 2006 database shows the following results when broken out by Prime Power rank:

 

     By: Prime Power Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1   -6749.10  50328.00    0.8659   7912  25164   .3144     2.5451 

        2   -7116.40  50750.00    0.8598   5096  25375   .2008     1.6256 

        3   -9084.80  50850.00    0.8213   3685  25425   .1449     1.1732 

        4  -11296.60  51400.00    0.7802   2750  25700   .1070     0.8662 

        5  -10256.50  50330.00    0.7962   2122  25165   .0843     0.6826 

        6  -12501.50  46618.00    0.7318   1424  23309   .0611     0.4945 

        7  -12865.70  38040.00    0.6618    912  19020   .0479     0.3881 

        8  -10811.80  27792.00    0.6110    517  13896   .0372     0.3012 

        9   -7103.70  18462.00    0.6152    306   9231   .0331     0.2683 

       10   -3618.30  11330.00    0.6806    167   5665   .0295     0.2386 

       11   -2095.50   5438.00    0.6147     74   2719   .0272     0.2203 

       12    -988.30   2542.00    0.6112     22   1271   .0173     0.1401 

       13    -269.90    556.00    0.5146      7    278   .0252     0.2038 

       14     -31.20    236.00    0.8678      3    118   .0254     0.2058 

       15      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       16      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       17      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       18      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       19      -4.00      4.00    0.0000      0      2   .0000     0.0000 

 

 

 

QRating

JRating and JPR aren’t the only comprehensive power ratings found in JCapper. JCapper2007 also has the QRating. Historically, each of the top four ranked QRating horses has outperformed the top four ranked Prime Power horses in both win percent and flat bet win roi. My calendar year 2006 database broken out by QRating rank looks like this:

 

     By: QRating Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1   -4323.50  49656.00    0.9129   7916  24828   .3188     2.5808 

        2   -6343.40  49660.00    0.8723   5150  24830   .2074     1.6789 

        3   -8006.70  49650.00    0.8387   3680  24825   .1482     1.1999 

        4   -9516.00  49604.00    0.8082   2789  24802   .1125     0.9102 

        5  -11757.50  48990.00    0.7600   2044  24495   .0834     0.6755 

        6  -12223.80  45972.00    0.7341   1421  22986   .0618     0.5004 

        7  -13316.40  38406.00    0.6533    890  19203   .0463     0.3752 

        8  -10418.90  28766.00    0.6378    546  14383   .0380     0.3073 

        9   -7921.50  19832.00    0.6006    300   9916   .0303     0.2449 

       10   -5791.70  12812.00    0.5479    153   6406   .0239     0.1933 

       11   -2906.60   6720.00    0.5675     69   3360   .0205     0.1662 

       12   -1752.50   3646.00    0.5193     34   1823   .0187     0.1510 

       13    -276.80    648.00    0.5728      4    324   .0123     0.0999 

       14    -234.00    310.00    0.2452      1    155   .0065     0.0522 

       15      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       16      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       17      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       18      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       19      -4.00      4.00    0.0000      0      2   .0000     0.0000 

 

 

 

 

JPR (JCapper Power Rating) and Win Rate

JPR is a comprehensive power rating. Historically, the top ranked JPR horse wins less often than the top ranked Prime Power horse. But this is by design. When I created JPR I made a trade off. I traded win percent for roi. My calendar year 2006 database broken out by JPR rank looks like this:

 

     By: JPR Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1   -4201.90  49656.00    0.9154   7401  24828   .2981     2.4129 

        2   -7015.80  49658.00    0.8587   4982  24829   .2007     1.6242 

        3   -8186.00  49658.00    0.8352   3676  24829   .1481     1.1984 

        4   -9533.10  49604.00    0.8078   2781  24802   .1121     0.9076 

        5  -12419.20  48986.00    0.7465   2150  24493   .0878     0.7105 

        6  -13247.30  45970.00    0.7118   1569  22985   .0683     0.5526 

        7  -12498.10  38410.00    0.6746   1078  19205   .0561     0.4544 

        8  -11488.90  28770.00    0.6007    641  14385   .0446     0.3607 

        9   -6677.70  19828.00    0.6632    372   9914   .0375     0.3037 

       10   -4872.70  12810.00    0.6196    212   6405   .0331     0.2679 

       11   -2885.10   6718.00    0.5705     86   3359   .0256     0.2072 

       12   -1497.70   3646.00    0.5892     41   1823   .0225     0.1821 

       13       9.80    642.00    1.0153      7    321   .0218     0.1765 

       14    -275.60    316.00    0.1278      1    158   .0063     0.0512 

       15      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       16      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       17      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       18      -2.00      2.00    0.0000      0      1   .0000     0.0000 

       19      -4.00      4.00    0.0000      0      2   .0000     0.0000 

 

 

JPR Numeric Value

I sometimes find it interesting to look at data samples broken out, not by factor rank, but by numeric value of a given factor. My calendar year 2006 database broken out by JPR numeric value is shown below. The really interesting thing to me is the correlation between JPR numeric value and win rate. Higher JPR translates quite nicely to higher win rate.

 

     By: JPR

 

     >=Min       <Max      Gain       Bet       Roi   Wins  Plays     Pct   Impact

   -999.00      15.00      0.00      0.00    0.0000      0      0   .0000   0.0000

     15.00      20.00      0.00      0.00    0.0000      0      0   .0000   0.0000

     20.00      25.00   -189.00    442.00    0.5724      2    221   .0090   0.0733

     25.00      30.00  -3609.40   6930.00    0.4792     73   3465   .0211   0.1705

     30.00      35.00 -10271.10  21624.00    0.5250    277  10812   .0256   0.2074

     35.00      40.00 -14365.70  33484.00    0.5710    618  16742   .0369   0.2988

     40.00      45.00 -11830.00  41884.00    0.7176   1112  20942   .0531   0.4298

     45.00      50.00 -15390.20  52294.00    0.7057   1782  26147   .0682   0.5517

     50.00      55.00 -12797.40  58736.00    0.7821   2598  29368   .0885   0.7161

     55.00      60.00 -10025.10  56374.00    0.8222   3382  28187   .1200   0.9712

     60.00      65.00  -7027.10  46124.00    0.8476   3657  23062   .1586   1.2836

     65.00      70.00  -4325.30  33626.00    0.8714   3411  16813   .2029   1.6422

     70.00      75.00  -2441.90  23190.00    0.8947   2948  11595   .2542   2.0580

     75.00      80.00  -1127.80  15418.00    0.9269   2352   7709   .3051   2.4697

     80.00      85.00   -907.00   9250.00    0.9019   1653   4625   .3574   2.8931

     85.00      90.00   -400.90   4312.00    0.9070    898   2156   .4165   3.3715

     90.00      95.00    -91.30    962.00    0.9051    225    481   .4678   3.7865

     95.00     100.00     -2.10     34.00    0.9382      9     17   .5294   4.2854

    100.00     105.00      0.00      0.00    0.0000      0      0   .0000   0.0000

    105.00  999999.00      0.00      0.00    0.0000      0      0   .0000   0.0000

 

 

 

JPR and Post Time Favorites

One of the more useful things to know when handicapping a race is the strength or weakness of the favorite. JPR numeric value is a great way to identify the true strength or weakness of post time favorites. There is a strong correlation between JPR numeric value and the win rate and roi of post time favorites. The following chart shows all post time favorites in my calendar year 2006 database broken out by JPR numeric value.

 

     By: JPR

 

     >=Min       <Max      Gain       Bet       Roi   Wins  Plays     Pct   Impact

   -999.00      15.00      0.00      0.00    0.0000      0      0   .0000   0.0000

     15.00      20.00      0.00      0.00    0.0000      0      0   .0000   0.0000

     20.00      25.00      0.00      0.00    0.0000      0      0   .0000   0.0000

     25.00      30.00      8.20     10.00    1.8200      2      5   .4000   1.1439

     30.00      35.00    -40.00     54.00    0.2593      2     27   .0741   0.2118

     35.00      40.00   -106.50    216.00    0.5069     18    108   .1667   0.4766

     40.00      45.00   -167.70    604.00    0.7224     72    302   .2384   0.6818

     45.00      50.00   -361.20   1312.00    0.7247    163    656   .2485   0.7106

     50.00      55.00   -827.80   2634.00    0.6857    309   1317   .2346   0.6710

     55.00      60.00  -1182.80   4938.00    0.7605    690   2469   .2795   0.7992

     60.00      65.00  -1604.10   7042.00    0.7722   1047   3521   .2974   0.8504

 

     65.00      70.00  -1645.70   8452.00    0.8053   1354   4226   .3204   0.9162

     70.00      75.00  -1077.20   8626.00    0.8751   1597   4313   .3703   1.0589

     75.00      80.00   -966.00   7822.00    0.8765   1542   3911   .3943   1.1275

 

     80.00      85.00   -564.80   6000.00    0.9059   1290   3000   .4300   1.2297

     85.00      90.00   -312.20   3474.00    0.9101    798   1737   .4594   1.3138

     90.00      95.00    -77.80    848.00    0.9083    210    424   .4953   1.4164

     95.00     100.00     -0.10     32.00    0.9969      9     16   .5625   1.6086

    100.00     105.00      0.00      0.00    0.0000      0      0   .0000   0.0000

    105.00  999999.00      0.00      0.00    0.0000      0      0   .0000   0.0000

 

 

Weak Post Time Favorite –red text – JPR of the post time favorite is below 65. The favorite wins less than 30 percent of the time. Take a stand against. Look elsewhere for value.

 

Normal Post Time Favorite –brown text – JPR of the post time favorite is 65 or higher but less than 80. The favorite wins between 32 and 39 percent of the time. Consider taking a stand against.

 

Strong Post Time Favorite –blue text – JPR of the post time favorite is 80 or higher. The favorite wins more than 40 percent of the time. Include or pass the race.

 

 

Negative Expectation Handicapping

One of the more interesting (and useful) ideas to come about from database handicapping is using the database to identify sets of horses to be avoided. In JCapper, this is done via the Negative Expectation UDM. I’m going to present a simple one factor Negative Expectation UDM that identifies a very high percentage of starters that are historically bad bets. The JCapper factor I’m going to use is CFA (Competitive Figure Ability.) My calendar year 2006 database broken out by CFA numeric value looks like this.

 

     By: CFA

 

     >=Min       <Max      Gain       Bet       Roi   Wins  Plays     Pct   Impact

   -999.00      70.00  -7455.50  27802.00    0.7318   1160  13901   .0834   0.6755

     70.00      70.50 -60892.70 187634.00    0.6755   6385  93817   .0681   0.5509

 

     70.50      71.00  -9805.20  59208.00    0.8344   3644  29604   .1231   0.9964

     71.00      71.50  -4392.00  31406.00    0.8602   2366  15703   .1507   1.2196

     71.50      72.00  -3381.70  20508.00    0.8351   1706  10254   .1664   1.3467

     72.00      72.50  -2209.60  14390.00    0.8464   1323   7195   .1839   1.4884

     72.50      73.00   -966.50  10376.00    0.9069   1113   5188   .2145   1.7366

     73.00      73.50   -813.50   8076.00    0.8993    908   4038   .2249   1.8202

     73.00      74.00   -419.90   5946.00    0.9294    692   2973   .2328   1.8841

     74.00      74.50   -599.90   4994.00    0.8799    595   2497   .2383   1.9288

     74.50      75.00   -621.60   4692.00    0.8675    562   2346   .2396   1.9391

     75.00      75.50   -432.90   3680.00    0.8824    459   1840   .2495   2.0193

     75.50      76.00   -421.00   2352.00    0.8210    289   1176   .2457   1.9892

     76.00      76.50   -443.80   2510.00    0.8232    316   1255   .2518   2.0382

     76.50      77.00   -377.30   2330.00    0.8381    320   1165   .2747   2.2234

     77.00      77.50   -160.30   1878.00    0.9146    292    939   .3110   2.5172

     77.50      78.00   -299.70   2246.00    0.8666    321   1123   .2858   2.3138

     78.00      78.50    -95.20   1146.00    0.9169    166    573   .2897   2.3450

     78.50      79.00   -167.50   1362.00    0.8770    209    681   .3069   2.4843

     79.00  999999.00   -845.50  12148.00    0.9304   2171   6074   .3574   2.8932

 

 

I used the UDM Wizard to create a Negative Expectation UDM named xCFA-Tossout. Running the xCFA-Tossout UDM through the Data Window against my 2006 database gives me the following results:

 

     UDM Definition: xCFA-Tossout

     Divisor:     999

     Surface Req: *  Distance Req: *ANY Distance*

 

     CFA:              MinRank= -999  MaxRank= 999  

                       MinVal= -999  MaxVal= 70.5  

                       MinGap= -999  MaxGap= 999

     Running Style:    ALL

 

 

     Data Window Settings:

     Divisor = 999   

     Filters Applied:

 

     Surface: (ALL*)  Distance: (All*)

    (From Index File: D:\2007\Q1_2007\pl_Complete_History_06.txt)

     Data Summary         Win     Place      Show

     Mutuel Totals  147087.80 143487.70 142386.10

     Bet           -215436.00-215436.00-215436.00

     Gain           -68348.20 -71948.30 -73049.90

 

     Wins                7545     16607     27193

     Plays             107718    107718    107718

     PCT                .0700     .1542     .2524

 

     ROI               0.6827    0.6660    0.6609

     Avg Mut            19.49      8.64      5.24

 

Go back up to the top of this document and compare these results to those shown in the very first chart where all horses in the database were shown using the ALL button. It should be very clear that xCFA-Tossout horses are horrible bets when compared to all horses in the database.

 

If I keep xCFA-Tossout around as an active UDM, whenever I run a Calc Races on race day, every horse with poor CFA will be marked by the xCFA-Tossout UDM on my HTML Report so that I can clearly see it. This gives me an easy way (using just one very simple UDM) to know which horses to throw out during my contender selection process.

 

 

CFA in a General Contender Selection UDM

Conversely, I can also use the UDM Wizard to create a CFA-Contender UDM where horses identified as CFA contenders are simply those not selected by the xCFA-Tossout UDM. After doing this in the UDM Wizard, here is what my calendar year 2006 database shows for CFA-Contenders.

 

     UDM Definition: CFA-Contender

     Divisor:     999

     Surface Req: *  Distance Req: *ANY Distance*

 

     CFA:              MinRank= -999  MaxRank= 999  

                       MinVal= 70.5  MaxVal= 999  

                       MinGap= -999  MaxGap= 999

     Running Style:    ALL

 

 

     Data Window Settings:

     Divisor = 999   

     Filters Applied:

 

     Surface: (ALL*)  Distance: (All*)

    (From Index File: D:\2007\Q1_2007\pl_Complete_History_06.txt)

     Data Summary         Win     Place      Show

     Mutuel Totals  162794.90 161682.30 160824.90

     Bet           -189248.00-189248.00-189248.00

     Gain           -26453.10 -27565.70 -28423.10

 

     Wins               17452     33191     46255

     Plays              94624     94624     94624

     PCT                .1844     .3508     .4888

 

     ROI               0.8602    0.8543    0.8498

     Avg Mut             9.33      4.87      3.48

 

 

     By: QRating Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1   -3717.50  42854.00    0.9133   7058  21427   .3294     1.7860 

        2   -4171.10  37168.00    0.8878   4115  18584   .2214     1.2006 

        3   -4798.80  31124.00    0.8458   2513  15562   .1615     0.8756 

        4   -4003.00  25700.00    0.8442   1659  12850   .1291     0.7000 

        5   -3566.70  19778.00    0.8197   1016   9889   .1027     0.5571 

        6   -1939.60  14144.00    0.8629    559   7072   .0790     0.4286 

        7   -1578.20   9052.00    0.8257    303   4526   .0669     0.3630 

        8   -1039.40   4932.00    0.7893    135   2466   .0547     0.2968 

 

        9    -891.60   2498.00    0.6431     58   1249   .0464     0.2518 

       10    -458.50   1284.00    0.6429     25    642   .0389     0.2111 

       11    -201.20    500.00    0.5976      8    250   .0320     0.1735 

       12     -55.50    182.00    0.6951      3     91   .0330     0.1787 

       13     -24.00     24.00    0.0000      0     12   .0000     0.0000 

       14      -8.00      8.00    0.0000      0      4   .0000     0.0000 

       15       0.00      0.00    0.0000      0      0   .0000     0.0000 

       16       0.00      0.00    0.0000      0      0   .0000     0.0000 

       17       0.00      0.00    0.0000      0      0   .0000     0.0000 

       18       0.00      0.00    0.0000      0      0   .0000     0.0000 

       19       0.00      0.00    0.0000      0      0   .0000     0.0000 

 

 

Before reading further, scroll back up to the top of this document and compare both the win rate and flat bet win roi of horses selected by this very simple UDM against the sample of all starters in the database. Note that using just a single factor yields a significantly higher win rate and roi compared to selecting horses at random. This should be a real eye opener to anyone who is not a horseplayer. I am constantly amazed by the sheer number of avid gamblers who will shun a game like horses where knowledge, experience, and hard work can yield a positive expectation – and opt instead to go to a casino and feed money into slot machines! But I’m getting off on a tangent here.

 

Back to the business at hand…

 

In the above chart I have purposely broken out CFA Contenders by QRating rank. I’ve purposely shown horses with QRating rank 9 and higher in red text because the data clearly shows these horses are historically bad bets. After adjusting the CFA-Contender UDM for QRating rank, here’s how the UDM fared against my calendar year 2006 database.

 

     UDM Definition: CFA-Contender

     Divisor:     999

     Surface Req: *  Distance Req: *ANY Distance*

 

     CFA:              MinRank= -999  MaxRank= 999  

                       MinVal= 70.5  MaxVal= 999  

                       MinGap= -999  MaxGap= 999

     QRating:          MinRank= 1  MaxRank= 8  

                       MinVal= -999  MaxVal= 999  

                       MinGap= -999  MaxGap= 999

     Running Style:    ALL

 

 

     Data Window Settings:

     Divisor = 999   

     Filters Applied:

 

     Surface: (ALL*)  Distance: (All*)

    (From Index File: D:\2007\Q1_2007\pl_Complete_History_06.txt)

     Data Summary         Win     Place      Show

     Mutuel Totals  159937.70 158392.40 157661.90

     Bet           -184752.00-184752.00-184752.00

     Gain           -24814.30 -26359.60 -27090.10

 

     Wins               17358     32951     45843

     Plays              92376     92376     92376

     PCT                .1879     .3567     .4963

 

     ROI               0.8657    0.8573    0.8534

     Avg Mut             9.21      4.81      3.44

 

 

     By: QRating Rank

 

     Rank       Gain       Bet       Roi   Wins  Plays     Pct    Impact

        1   -3717.50  42854.00    0.9133   7058  21427   .3294     1.7530 

        2   -4171.10  37168.00    0.8878   4115  18584   .2214     1.1784 

        3   -4798.80  31124.00    0.8458   2513  15562   .1615     0.8594 

        4   -4003.00  25700.00    0.8442   1659  12850   .1291     0.6871 

        5   -3566.70  19778.00    0.8197   1016   9889   .1027     0.5468 

        6   -1939.60  14144.00    0.8629    559   7072   .0790     0.4207 

        7   -1578.20   9052.00    0.8257    303   4526   .0669     0.3563 

        8   -1039.40   4932.00    0.7893    135   2466   .0547     0.2913 

        9       0.00      0.00    0.0000      0      0   .0000     0.0000 

       10       0.00      0.00    0.0000      0      0   .0000     0.0000 

       11       0.00      0.00    0.0000      0      0   .0000     0.0000 

       12       0.00      0.00    0.0000      0      0   .0000     0.0000 

       13       0.00      0.00    0.0000      0      0   .0000     0.0000 

       14       0.00      0.00    0.0000      0      0   .0000     0.0000 

       15       0.00      0.00    0.0000      0      0   .0000     0.0000 

       16       0.00      0.00    0.0000      0      0   .0000     0.0000 

       17       0.00      0.00    0.0000      0      0   .0000     0.0000 

       18       0.00      0.00    0.0000      0      0   .0000     0.0000 

       19       0.00      0.00    0.0000      0      0   .0000     0.0000 

 

 

Using just two factors, we have been able to create a simple UDM that does a fairly good job of identifying contenders. I can assure you that this just barely scratches the surface of what is possible. Given a little time and effort, I have no doubt whatsoever that you can use the tools in JCapper to greatly improve upon what I have started to present here.

 

There are hundreds of factors and filters in JCapper… far too many for me to show Data Window results for all of them in this Help Doc. Some of the more popular ones among JCapper users are CFA, AFR, CPace, PctE, PMI, Form, Stamina, and WoBrill. Like I said before, once you build some databases of your own you will undoubtedly want to run them through the Data Window yourself.

 

My main purpose in writing this Help Doc was to show you a few of the ways that using a database can help you in your handicapping.

 

 

Jeff Platt

March, 2007