Why Use Our Method?

Past Experience

The approach used has been tested during the past four baseball seasons with encouraging results. We have had positive outcomes each year.

Why the method makes sense

You might be wondering why we think our method could do this when our expertise is in mathematics and not in baseball. Those that play poker games,  scoffed at mathematicians as well until math geeks started winning major poker titles. Others like to play games that can be analyzed mathematically, like Jackpotjoy.com's 90 ball bingo online game. In any case, when the payoffs are set each day for each team in each contest, these payoffs are based on the amounts bet on each team by bettors. We do not suppose that we know any more than baseball insiders. However, in order to win in the long run, we just need to be better than the average bettor. (One can use the same analogy in playing the stock market. In order to do better than the averages, one does not need to have any inside knowledge. One just needs to understand more than the average investor.)

How our method works

We have a copyrighted mathematical model for analyzing the probability a given batting order (with a particular pitcher on the mound) has of beating another lineup and their pitcher. This probability implies what the betting line for that lineup should be on the given day. If our model's suggested betting line does not agree with the betting line available, the method suggests betting on one team or the other. On the other hand, if our probability lies within the spread, we suggest not betting on that game.

Various games are available at the here for above average betting ability.

We include below a table relating betting lines to probabilities.

Changes for the 2003 season

In August 2002, we found a silly coding error that meant all the results from earlier in 2002 and all of those for 2001 were in error. In the off-season, we re-ran the whole season after fixing the bug (Aug. 20 until the end of the season were correct). For the season, the method would have been up 640 units -- using the 6% cutoff described in the explanation for the 2002 season.

We thought that rather than using a set percentage error factor, it might make more sense to bet only on games for which the expected value of the winnings is greater than a given value. We performed an analysis of the 2002 season results and came up with the table below.

It seems that a reasonable balance between wanting to recommend a goodly number of gains while maintaining positive results would be only to recommend those games for which we have an expectation of gaining 17 or more units. We toyed with the idea of recommending games with regions of expectations (e.g. 18 to 30 and greater than 35) but since we have no explanation for why the results should be better for 18-30 but not slightly above, and the output gets more complicated, we decided to keep things simple. (It turns out that there is a negative expectation region for which the results were quite positive -- but, of course, we cannot explain that either). We have used this expectation for the 2003 and 2004 seasons and plan to use it for the 2005 season.

Changes for the 2002 season

In 2001, we recommended betting on a team if the payoff (even if our model had an error of 2%) was favorable. An analysis of that season's results indicated that we would have done better (and much better per game) had we used an error allowance of 6%. We would have been ahead by more, while betting on less than half as many games. We expect to recommend bets on far fewer games, but hope the results will be better per bet.

Analysis of 2001 results:

See the table below for the results of the analysis. The percent column gives how much  error we allow our method to have before deciding if the bet is favorable.  E.g., the bet is favorable if our model says the team has a 60% probability of winning, we have a 10% margin of error and the payoff is +100 or better -- bookie says probability is less than 50%. That is, we bet when our probability is that percent or more above the bookie probability based on the payoff. (In the analysis, we did not consider games with no data, low data, pitching changes and "over the limit" games).

Based on the table below, we decided that a buffer of 6% allows for us to recommend plenty of games with a reasonable amount of winnings per bet. Notice, that the numbers of wins/number of losses is around 1, while the winnings were positive. That is because the model tends to recommend more underdogs than favorites.