Baseball Totals

[DougOzzzz]

The average AL team in 2004 scored 5.01 runs per game. In the NL, the figure was 4.64. Combined, the average is about 4.83 RPG. All games from 2004 averaged a total of about 9.65 RPG.

If you go to pinnaclesports.com and look up the over/under lines, most totals are set at 9 runs or fewer. Today, the average line is 8.625 with slightly more juice on the over (-108 vs. -102).

If baseball totals are set more than a full run below the average run scoring, wouldn't it make sense to bet the unders consistently?

To test this theory, I set up an Excel spreadsheet. Using a formula from Baseball Prospectus 2005 (developed by Tangotiger), I estimated the frequency of 0, 1, 2, 3, 4, 5.... 20 run innings based on the number of runs per game for each team. The formula is very accurate when compared to actual statistics. I then "simmed" 200,000 games using these figures. The game ended if the home team was ahead after 8.5 innings, or if any team led after 9+ complete innings. If a home team happened to have a "big" inning in the 9th or later and won by more than 2 runs, I changed the runs scored so that 1 run wins outnumbered 2 run wins by a ratio of 5 to 1, and 3+ run wins were nonexistant (they do happen occasionally, but are rare). It's not perfect, but it does give a pretty adequate representation of the distribution of run totals for each team.

Both teams were set at the league average figure of 4.83 runs per game. The home team ended up scoring a bit less than that (since some big innings were taken away from them), while the road team scored a bit more than that. Interestingly enough, the home team still won 52% of the games - I cannot see a reason for this.

Anyways, a few conclusions based on the results (these should not be anything new to those who are familiar with betting on baseball totals):

1. 54.6% of all games ended with a total runs scored of 9 or fewer. You'd expect that number to be significantly less, considering the average is 9.65.

2. On average, a total of 9 would return 44.5% wins, and 45.4% losses. Thus, the totals according to this should be set slightly BELOW 9 runs on average. This is consistent with the lines at pinnaclesports.com, though this particular testing would probably assign an average line of ~8.9 runs rather than 8.625.

3. Odd run totals are significantly more frequent than even run totals. Again, this should be no surprise to anyone familiar with baseball totals. Odd run totals accounted for 58.95% of the possible outcomes - about 43.6% more often than even run totals.

4. The home team averaged .06 fewer RPGs than the road team. This doesn't sound right to me. About 20% of games should end after 8.5 innings, meaning the home team should lose about .2 innings per game. That should be equal to .11 runs, not .06.

5. The reason that these games usually have 9 or fewer total runs is obvious. There is no upper limit for total runs, yet there is a lower limit (1 run). Thus, there needs to be a greater frequency of low scoring games to balance the higher scoring games. About 4.45% of games ended up with 18 or more runs scored. These accounted for 187,793 runs, or 0.938965 runs/game (or about 9.7% of the total runs). Subtracting that from 9.65, we get 8.71 - which is much closer to the break-even point than 9.65.

Anyways, just thought this was interesting. If you think 2 teams are likely to average X amount of runs per game, yet the line is set BELOW X, it may not be profitable to bet the over. If anyone has actual data relating to this, I'd be interested to see how close my numbers match up.

[tech]

Averages (means) are worthless, especially when dealing with discrete data such as baseball totals.

[lastsamurai]

I agree... and with the player all natural this year...i see alot of HR totals down from last year.

[Tboner7]

There were 9 games yesterday with run differences of 3 or more. That's not rare.

[DougOzzzz]

[ QUOTE ]
Averages (means) are worthless, especially when dealing with discrete data such as baseball totals.

[/ QUOTE ]

I don't understand how this is true. Averages (means) have the advantage of being (relatively) easy to project. You take a teams expected RS (based on the lineup and season stats), you adjust for opposing pitching/defense and park effects, and you get a figure. You do this for both teams, then plug it into the formula, and see how often the totals fall above/below X amount of runs.

I fail to see how that is not useful, unless my formulas are incorrect. I concede that they are not perfect - a team can't be expected to have the same number (mean) of runs per inning in the 1st inning compared to the 2nd, or the 8th (against a reliever), etc. But I think overall it comes pretty close.

[tech]

I'm not saying your method is worthless -- I really haven't looked at it enough to know. My point is that you need to use a different statistic. Use medians, modes, frequency distributions, whatever. But the means are going to mislead you here. One or two outliers is going to throw you off significantly with a sample size of only 20 or so games.

[DougOzzzz]

Of course I agree with that. The whole thing is based on coming up with ACCURATE projections for future runs per game. Twenty games, or even 80 games, isn't enough.

As for coming up with these projections - I'm not quite there yet. But it will probably be a combination of PECOTA projections and current season stats.

[sportypicks.com]

Tech is right,

Averages are the lazy man's way of handicapping sports and they are totally misleading.

[Paluka]

Your post seems really obvious to me. It would be a pretty stupid assumption to think that just because something on average is 9.5 that the results are necessarily distributed 50% below and 50% above.

[sportypicks.com]

Doug,

I'm glad that you post topics like this as I prefer reading topics on methodology rather than the run of the mill stuff you get on most message boards (Play this, fade this, etc.).

As far as your measuring ballpark affect goes, I believe you can get data like this off of statistical sites out there. Otherwise you can always create it yourself depending on the purpose you are using it for. Ballparks are something that don't change from year-to-year like teams do, so you can use a few years of data to calculate an accurate ballpark factor. I wish you well in your simulations, and I'm interested in hearing how you deal with bullpens.

Bullpens is what took the longest amount of time in our handicapping methods. I don't believe there is a "right" answer and I'm sure there are many ways to deal with it.

Good stuff.