Mlb betting strategies using stats

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Predicting the outcome of a baseball game requires breaking down all kinds of different numbers if you want to have the best chance to win in the long run. ERA is the number of earned runs a pitcher allows per nine innings pitched. WHIP — This stat is defined by the amount of walks and hits per inning pitched. A good WHIP is anything at 1. The more hitters a pitcher allows to reach base, the more chances he has to give up runs. A pitcher with a 1. You want to back ground ball pitchers because they have less chance of giving up a home run, especially in hitter-friendly parks.

You certainly want to back strikeout pitchers against teams that are prone to the strikeout. This stat is defined by how many home runs a pitcher gives up per nine innings. Obviously, the more home runs you give up, the more runs you will allow. Pitchers with a high batting average on balls in play have been unlucky, while pitchers with a low batting average on balls in play have been lucky. BABIP tends to even out.

You can ignore one bad start sometimes, but if a starter has three in a row, that is a trend that will likely continue. Head-to-Head — Seeing how a starter has fared against his opponent throughout his career is usually a good indicator of how he will do in his next start against that same opponent.

Similarly, teams that got blown out a lot should have better personnel the next year. As an aside, one-run games are a big deal when it comes to Pyth W-L. Generally, teams are within a couple of games above or below. Significant outliers are likely to regress the following season. The same six individual outcomes produced two very different outcomes overall.

BaseRuns eliminates that element of randomness and spits out a measure of runs per game and runs allowed per game given all of the individual outcomes. Then, it produces a win-loss record. It stands for Weighted On-Base Average. Unlike its predecessor, on-base percentage, wOBA assigns a weighted value to each way of reaching base. When it comes to on-base percentage, there is no distinction between a single or a home run.

The weights of the outcomes are assigned based on the offensive climate around Major League Baseball. For example, the weight of a home run was 1. Walks, however, were the highest since at. Quite simply, wOBA actually distinguishes between the value of ways of getting on base, thus making it better than most every other offensive metric. Those are important stats for hitters, but I will use them more frequently with pitchers.

Keep in mind that traditional batting average factors strikeouts into the equation because those are at bats. BABIP is a good measure of luck. The same can be said about pitchers in terms of contact quality and luck. It is such a tremendously flawed statistic. FIP is a better metric and one that I use often. It takes the defense out of the equation.

FIP takes that element out of the equation. It is all subject to variance, especially once it is put in play. Last season, obviously, we had a significant number of home runs hit. It was just The relevance of that will become clearer as we move forward. Sometimes pitchers are getting unlucky with fly balls that hit a jet stream or just keep carrying.

Other times, they are simply making bad pitches. Like any statistic, we have to dig deeper to find out the root cause, but xFIP is a good predictor of future performance. It eliminates some of the noise of small sample sizes. This is how we get a little bit deeper.

Think about it. Pitchers that allow a lot of line drives are going to give up more hits. Hard contact is a bad thing, no matter how good the defense is. The big thing about these stats is that they carry predictive value. One of the last great frontiers to explore for baseball stat geeks like me is defense. Errors are a poor stat. They only count if the fielder gets to the ball and are based on subjective discretion by the official scorer.

Because fielding percentage uses errors, it is also a poor measure of defensive ability.

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