Zoltar is my NFL football prediction computer program. It uses custom reinforcement learning and a neural network. Here are Zoltar’s predictions for week #16 of the 2021 season. It usually takes Zoltar about four weeks to hit his stride and takes humans about eight weeks to get up to speed, so weeks six through nine are usually Zoltar’s sweet spot. After week nine, injuries start having a big effect.
Zoltar: titans by 6 dog = fortyniners Vegas: titans by 1
Zoltar: packers by 7 dog = browns Vegas: packers by 7
Zoltar: cardinals by 2 dog = colts Vegas: cardinals by 5
Zoltar: falcons by 6 dog = lions Vegas: falcons by 6.5
Zoltar: buccaneers by 4 dog = panthers Vegas: buccaneers by 11
Zoltar: ravens by 0 dog = bengals Vegas: bengals by 2.5
Zoltar: chargers by 2 dog = texans Vegas: chargers by 10.5
Zoltar: rams by 0 dog = vikings Vegas: rams by 3.5
Zoltar: patriots by 4 dog = bills Vegas: patriots by 2
Zoltar: jets by 5 dog = jaguars Vegas: jets by 1.5
Zoltar: eagles by 6 dog = giants Vegas: eagles by 9
Zoltar: seahawks by 6 dog = bears Vegas: seahawks by 7
Zoltar: chiefs by 6 dog = steelers Vegas: chiefs by 9.5
Zoltar: raiders by 5 dog = broncos Vegas: raiders by 1
Zoltar: cowboys by 6 dog = redskins Vegas: cowboys by 9.5
Zoltar: saints by 4 dog = dolphins Vegas: saints by 3.5
Zoltar theoretically suggests betting when the Vegas line is “significantly” different from Zoltar’s prediction. In mid-season I usually use 3.0 points difference but for the first few weeks and last few weeks of the season I go a bit more conservative and use 4.0 points difference as the advice threshold criterion. In middle weeks I sometimes go ultra-aggressive and use a 1.0-point threshold.
Note: Because of Zoltar’s initialization (all teams regress to an average power rating) and other algorithms, Zoltar is much too strongly biased towards Vegas underdogs. I need to fix this.
For week #16:
1. Zoltar likes Vegas underdog Panthers against the Buccaneers
2. Zoltar likes Vegas underdog Texans against the Chargers
3. Zoltar likes Vegas underdog Vikings against the Rams
4. Zoltar likes Vegas underdog Steelers against the Chiefs
5. Zoltar likes Vegas underdog Redskins against the Cowboys
6. Zoltar likes Vegas favorite Titans over the 49ers
7. Zoltar likes Vegas favorite Jets over the Jaguars
8. Zoltar likes Vegas favorite Raiders over the Broncos
For example, a bet on the underdog Panthers against the Buccaneers will pay off if the Panthers win by any score, or if the favored Buccaneers win but by less than the point spread of 11.0 points (in other words, by 10 points or less). If the Buccaneers win by exactly 11 points, the bet is a push.
Theoretically, if you must bet $110 to win $100 (typical in Vegas) then you’ll make money if you predict at 53% accuracy or better. But realistically, you need to predict at 60% accuracy or better.
In week #15, against the Vegas point spread, Zoltar went 3-2 (using the standard 3.0 points as the advice threshold. Overall, for the season, Zoltar is 51-44 against the spread (~53%).
Just for fun, I track how well Zoltar does when just trying to predict just which team will win a game. This isn’t useful except for parlay betting. In week #15, just predicting the winning team, Zoltar went 10-6 which is about average.
In week #15, just predicting the winning team, Vegas — “the wisdom of the crowd” also went 10-6, the same as Zoltar.
Zoltar sometimes predicts a 0-point margin of victory, which means the two teams are evenly matched. There are two such games in week #16. In those situations, to pick a winner (only so I can track raw number of correct predictions) in the first few weeks of the season, Zoltar picks the home team to win. After that, Zoltar uses his algorithms to pick a winner.

My prediction system is named after the Zoltar fortune teller machine you can find in arcades.
Left and Center: A few years ago, I was working with speech recognition. I created a small Zoltar machine (about 12 inches high) that accepted voice commands (“Will the Rams beat the Bears?”) and displayed his answers via colored lights on a crystal ball (a marble). The speech recognition and prediction systems ran in some old laptop hardware I scavenged and kludged together.
Right: I’ve experimented with adapting Zoltar’s underlying prediction algorithms to college basketball. That system is named Zoltara.

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