Like games - setting minimum number of games desired

Now it is time to deal with the question of what to set the minimum number of like games to be used. This is a parameter to the Like Game Routine. The purpose of the system is to identify a set of previous games most like today’s game, and hence predict likely final scores. One technique is to look at the correlation of the average scores of the games in the set compared to the actual final scores. 

I wrote a routine to set the minimum number of games at 50, then process each of the games in the past 11 years of data and calling the find like games routine. I saved the average scores from the like games, the expected scores from the spreads and totals, and the final scores. I computed the correlation between these numbers. I repeated this for 100 games, then 150 games, and so on.

Following is a summary for the NFL.

# Min Games	Corr1	Corr2	Corr3	Corr4
50	0.381	0.383	0.360	0.361
100	0.376	0.373	0.360	0.361
150	0.369	0.364	0.360	0.361
200	0.367	0.362	0.360	0.361
250	0.366	0.359	0.360	0.361
300	0.365	0.358	0.360	0.361
350	0.359	0.357	0.360	0.361

Corre1 and Corr2 are the correlations for the like games versus the actual scores. Corr3 and Corr4 are the correlations of the expected scores from the lines and the actual scores. An interesting note is that at 50 games, the like game scores are a better predictor than the lines. It’s not until about 200 games that the lines catch up.  I’ve decided to use 200 games because each score in the selected set will only have a 0.5 % impact on the probabilities, while at 50 games each would have a 2% chance.

Here is the comparable chart for CFB.

# Min Games	Corr1	Corr2	Corr3	Corr4
50	0.592	0.596	0.583	0.590
100	0.587	0.593	0.583	0.590
150	0.586	0.590	0.583	0.590
200	0.584	0.589	0.583	0.590
250	0.583	0.588	0.583	0.590

Again, the like games are a better indicator than the lines at the lower end. The line doesn’t catch up until about 200-250 games.

In my next post, I’ll put the like game system to work in looking for NFL money line wagers. I’ll post on Twitter, @ole44bill, when I post. If you have comments or questions add them to the blog or Email them to me at ole44bill@gmail.com. The forum at sportsbookreview.com is no longer an option. The thread I started on this topic has been deleted. Apparently, I violated a rule by listing a link to my blog.

Like games - matching routine

This post deals with how to identify the set of games most like today’s game. I have written a routine that will do that. When first encountered for a sport, the history files for that sport are read and an internal table is built. The internal table contains the expected scores for each team (computed from the spread and total) and the final score for the game. (Note: these are big tables, 2,854 games for the NFL and 7,823 games for CFB.)

The calling code supplies 3 variables, the 2 expected scores for today’s game and the minimum number of games desired. The routine then passes every entry in the table computing the distance between today’s game and the past game for that entry. Remember, consider each pair of expected scores can be considered a point on a X/Y graph. The distance is computed as the square root of the sum of the respective difference in the scores squared.  If this is reminiscent of the Pythagorean theory, there is a good reason for that.

These distances are saved in another array, which is sorted by increasing distance from today’s game. Finally, the minimum number of desired games (plus distance ties) are returned to the calling code. Then for any potential wager, these set of actual scores can be counted, percentages calculated, and expected return computed.

I hope this is a clear explanation of what occurs. If not, feel free to ask a question on this blog, Email at ole44bill.gmail.com, or in the “Handicapper Think Tank” forum of www.sportsbookreview.com under the topic “Like game system”. My next post will deal with the question of how to determine what a good minimum number of games is. I’ll tweet on Twitter, ole44bill, when I post it.