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Creating projective scores makes $ense

Nov 4, 2003 4:29 AM

For handicappers who rely to some extent on statistical analysis, rather than trends, injuries or motivational factors, the most common approach is to create a formula for projected scores.

Whether you use power ratings, yards per point numbers, or more sophisticated data sets, the eventual output is designed to be "Team A projected to beat Team B by six points."

Once a prediction is decided, you can compare that to the actual spread to see where the betting value lies. An "overlay" refers to the perceived value between your expectation of what will happen and the actual spread. If you think Team A will win by 10 and they is only favored by 3, you have a 7 point overlay betting on A. Whether you have an actual overlay depends on your predictions being more accurate than the point spread.

This is all well and good and constitutes a solid approach to picking winners.

One of the drawbacks to projecting scores is that using performance averages may not reflecting the true range of possible outcomes. Consider these outcomes in a match up where team A is a 10-point favorite over B:

A wins 35-10 (A covers), A wins 28-21 (B covers), A wins 17-14 (B covers). If those scores were an accurate representation of the range of possibilities, then B would actually cover 2 of 3 times. However, your average projected score would A winning 27-15. From that, you would think team A was the better bet!

One solution to this problem is creating a program simulating each match up thousands of times and tracking the range of outcomes.

Simulation programs can be very simple or complex. If you grew up playing Strat-O-Matic dice games, or have dabbled a bit on a PlayStation video game machine, you can probably imagine the concept of repeatedly playing a game to see which teams wins in the long-term.

The problem with trying to simulate a match up on a video or dice game is not having the current season up to date stats available. Plus, the algorithms underlying the games may not be very realistic.

In particular with the NFL, the way the game unfolds has a huge influence on the final score. Teams change strategy based on situations. For instance, when a team is ahead it will often become more conservative on offense. That leads to fewer points per drive, but also fewer turnovers. Conversely, a team that trails will tend to get more aggressive. This results in more big gains, but additional mistakes.

A good simulation program should adjust for this effect. This is different from a sport like baseball, where each at bat is not greatly affected by what has occurred before except if a different pitcher is used.

Also, simulation engines may be the ultimate tool for besting the spread since they can get at the match up specifics. The downside to power ratings is that teams get the same rating regardless of the opponent. However, if your simulator is at the play-by-play level of detail, then the expected performance will produce a big difference when a passing team faces a good pass defense.

The best part of simulation programs is that they can give both a pick for the game and an expected cover percentage based on your line. No more head scratching to deduce the "strength" of a pick. It’s there in black and white. When calculating lines moves, it would be nice to know when it is worth buying an extra half-point.

Reading results is one of the best features. In Tampa Bay at San Francisco, against a SF +3.5 line, the 49ers covered in 56 percent of the simulated games. If you shopped around and found a +4 line, the cover percentage rose to 58. On the other hand, if the best line was +3 then San Francisco had a 52 percent loss against the vig wager.

If you think about games in terms of the range of events that can transpire, rather than pegging all your thinking on one specific score, you can better understand teams may be better on paper but wind up being losing bets over the long term facing a large spread.

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