We’re 62% (http://www.bestofblog.net/nfl_picks_2009/) ATS and doing very poorly with our Over/Unders. In addition, there is a correction factor that is involved in our Over/Unders which may not be such a good assumption. We’ve temporarily suspended our over/unders.
- Happy
Week eight saw us go 7/9 on bet units, returning to the right side of the Expectation Value Probability Distribution for the season. We are now running 60% (53/89) for the season on bet units.
I know of at least two websites (mine and NFLPickles.com) that post absolutely free NFL picks with no membership required that consistently beat the spread. It got me wondering, why do most sports bettors still lose in the long run?
I avoided psycho-babble when I posted on Setting Realistic Expectations for Sports Betting, but now I have to resort to it. People want to feel responsible for their success. The ratio between the standard deviation and the expectation value is such that it is unlikely for an everyday Joe to make enough money to be more valuable that the entertainment he gets from making his own picks.
To illustrate, our system posts an ROI of 10 – 13%. If Joe Public bets $50 with our system, he can expect a $5.00 – $6.50 profit. If he used his own system, assuming it runs at 50%, he can expect a $2.50 loss. But the entertainment value he receives from making his own picks is worth more than the $7.50 – $9.00 change in expectation.
Big bettors are probably not too much better off, unless they have a gambling problem. If you can afford to risk $500 on any given week, then you can also probably afford to toss $75 – $90 in expectation for the joy of making your own picks.
This got me thinking further – there’s a middle ground. What if a person were able to use their own intution applied to a selection of statisticians offering free picks? NFLPickles is running over 70% ATS YTD, Happy is running 60% with a more statistically distinct sample size, and he also posts BOBB picks based on Brian Burke (NY Times sportswriter who publishes weekly NFL Probabilities). Since these three don’t always agree, a person may be able to select between their picks, combining an element of free choice (and responsibility) with solid statistical analysis.
I haven’t computed the statistics on when our three models agree and when they disagree, etc. I do know we all had Jacksonville today ) : but we also all had Dallas. The idea of providing choice between several different successful oddsmakers is still appealing. It gives some freedom to the gambler while still yielding a positive expectation.
We reformatted our week seven free NFL picks page. We’re thinking of doing our entire NFL Oddsmakers Free Picks site with the new format. Currently most of the pages (look at past week’s picks) are still do the old format. Which do you prefer?
Other ideas for how to make our site more attractive / user-friendly?
Thanks,
Happy
Before I created my NFL Predictive Model for forecasting the outcomes of NFL games, I always thought betting the Over/Under was just not all that interesting. What really matters is who is going to win the game, right? Coming off NFL week six where my over/under bet units were 5/5 (and my line bet units were only 3/6) I thought it might be a good time to sing the praises of the over-under bet.
The over bet is one bet that can be put in the bag before the game is over. In fact, during the Saints vs Giants game I’d already won one of my best bets before half-time! The under can start looking pretty sure by midway through the fourth quarter. Recently examples include under in the Bills vs Browns and under in the Chiefs vs Redskins (both games in which our model best bet the under).
By contrast, the soonest you can count your un-hatched chickens in a spread bet is at the end of regulation when you’ve taken a huge underdog (we notched the Bills vs Jets as a victory at the begging of overtime because we had 9.5 points).
On a more philosophical note, the OU is also more a reflection of the character of the game. We were glad our model took the over in the NO vs NYG game because we thought the game pitted great offenses against decent defenses and that it would be a shootout. As far as who would win, who knew? The model has NO by 2.7 points but with zero confidence.
Adding the Over Under to our offerings has increased our bet-worthy events from 3-5 per week to 6 – 10 per week. The increase in bet-worthy events narrows the probability distribution of our expectation values, increasing our odds of a winning week. Note that even though our individual weeks range from 63% – 70% odds of winning, YTD we have a 76.5% chance of winning (and each event is only 55% – 60%). The more events we can add, the faster we narrow the distribution around our 110% expectation value – making winning a more certain long-term outcome.
I’ve seen many people attempt to answer the question, “How do I convert a point-spread to a money-line?” (or conversely, “How do I convert a money-line to a point-spread?” The answers I’ve seen given have been unequivocally bad; but that’s because the correct answer is not simple, and people like simple answers.
To understand that a simple plug-and-play formula won’t do the trick, just check out the lines at your favorite sportsbook. I checked betus.com at 8:00 PM on the 16th of October and found the following lines:
St. Louis +9.5 -110 (points) or +400 (moneyline)
Jacksonville -9.5 -110 (points) or -500 (moneyline)
Buffalo +9.5 -110 (points) or +350 (moneyline)
NY Jets -9.5 -110 (points) or -450 (moneyline)
From this it is clear that there is more involved than simply knowing the point-spread to determine the moneyline (or more than just knowing the odds to determine the pointspread).
The function that is used to convert from a pointspread to a moneyline is the normal distribution function . This function must be integrated from negative infinity to the point-spread to determine the probability of an outcome. In all cases the mean () is zero. If the pointspread is -9.5 that is the value used for x. The value of the integral from –∞ to -9.5 is also a function of the standard deviation; and this is what varies from game to game.
We can back-calculate the standard deviation the casino used by iterating (such as tools –> goal-seek from MS Excel). In the game where Jacksonville is -500 you have to lay $500 to win $100. But remember the 10% juice. Without it you’d only have to lay $450 to win $100. This means that Jacksonville should win 4.5 times more often than it loses. Neglecting ties this means that Jacksonville should win 4.5 / 5.5 of the contests or 81.8%. Taking the inverse of the integrated normal distribution function (=norminv(0.818, 0, guess) in MS Excel) you can change your guess (using tools –> goal-seek) to get a point-spread of 9.5. In the case of the Jacksonville vs St. Louis game you’ll find that the sportsbook used a standard deviation of 10.46. You can now verify this by using the same standard deviation but a negative spread; you’ll find St. Louis has a 18.2% chance of winning. One divided by 18.2% = 5.5. This means that the casino without juice would give you $450 winnings for every $100 bet (you get $550 off $100 18.2% of the time). Since they take their juice off your winnings, the line should be 90% of 450 or 405. Note that they’re actually offering +400. I have a feeling rounding errors tend to favor the house!
A similar exercise will reveal that the standard deviation used in the Bills vs Jets game is 11.2. I would have simplified to assume that the standard deviation tracks the over/under, but that is clearly not the case here. The casino most likely has team specific standard deviations (due to a more complex model than mine).
This brings up a separate, but related issue. If the casino’s model is more complex than mine, how can I expect to win in the long run? The key is that I don’t have to be smarter than the sportsbook. The book is balancing their desire for profit (holding their line) with risk mitigation (splitting the betting public evenly). If I have a decent model (I’m right greater than 52.4% of the time) I can win in the long run. Currently we’re running 58% season to date.
We’ve updated our NFL Free Picks Results vs Expectation Values to include Week five results.
We’ve also now posted our NFL week six free picks which include five (5) bet worthy Week 6 NFL lines and three (3) bet worthy NFL Week Six over unders. There are three best bets (Ten +9.5, over 47 in NO vs NYG, and Under 37 in WAS vs KC). The expectation value distribution has return to a near normal distribution but the predicted ROI of 106.6% is the lowest of the season. Don’t be too discourage though; this is in part just because we’ve become more conservative in using an adjusted cumulative distribution function as our odds of winning each event.
Our Free NFL picks require no registration or membership, are not accompanied by annoying popup windows, and are concisely located on a single page for each NFL week.
During NFL Week 5 we were 64% on bet units (58% season to date). We are 11/16 (69%) on best bets for the season.
Our picks are based on a statistical model that generates predictes outcomes using multiple systems and compares them to multiple casino lines. We use the delta (large is good) between the model spread and the casino spreads along with the population standard deviations (small is good) to arrive at confidence factors.
We also compute the Cumulative Distribution Function (Z-statistic) for each pick to predict a likelihood of success. You need 52.4% to overcome 10% juice and 51.2% to overcome 5% juice. Our bet units are based on both the Z-statistic and the confidence.
For more details, be sure to visit Happy’s Free NFL Picks.
NFL Week Five bet units are in the bag; we finished 7/11 on our bet units for the week. It looked like Houston would pull it off and make us 10/11 (we had three units on them), but still a respectable week for us. It brings us to a 109.5% ROI for the 2009 NFL Season so far and completes our fourth winning week out of five.
We want to give props to the first other statistically based pick system we’ve seen on the net, advancednflstats.com. I guess they’re technically a competitor, but its just refreshing to find someone other than us posting useful statistically based probabilistic data on NFL outcomes.
We are able to and would like to convert their probabilities to ATS predictions; we’re awaiting their permission to do so (or they may just choose to do it themselves). Anyhow, we’ll post week six picks on Wednesday or Thursday.
- Happy
We’ve updated our NFL Free Picks Results vs Expectation Values to include Week four results.
We’ve also now posted our NFL week five free picks which include three (3) bet worthy Week 3 NFL lines and two (2) bet worthy over unders. There are three strong best bets (Minnesota -9.5, Houston +5.5, Under 40.5 in the Buf vs Cle) which has resulted in a less smooth than normal distribution but a good predicted ROI of 113.7%.
Our Free NFL picks require no registration or membership, are not accompanied by annoying popup windows, and are concisely located on a single page for each NFL week.
During NFL Week 4 we were 55% on bet units (56% season to date). We are 9/13 (69%) on best bets for the season.
Our picks are based on a statistical model that generates predictes outcomes using multiple systems and compares them to multiple casino lines. We use the delta (large is good) between the model spread and the casino spreads along with the population standard deviations (small is good) to arrive at confidence factors.
We also compute the Cumulative Distribution Function (Z-statistic) for each pick to predict a likelihood of success. You need 52.4% to overcome 10% juice and 51.2% to overcome 5% juice. Our bet units are based on both the Z-statistic and the confidence.
For more details, be sure to visit Happy’s Free NFL Picks.
Psychologists and gaming experts have estimated that only about one out of every one hundred gamblers are profitable. So why do the other 99 keep gambling? There are many reasons but as statisticians we’re going to focus on one so that we can avoid falling into the same trap.
Ignoring the psycho-babble that says losing makes people feel more alive, we believe that people continue to lose because they believe they’re winning. This is primarily because of the high standard of deviation and the low delta needed to make or break the typical sports better. (Most, if not all, popular gambling relies on the standard of deviation to fool losers into believing they can win).
In order to avoid falling into the same trap, we have to be able to set realistic expectations and then analyze our actual results to determine if they are in the same statistical population as our expectations. If every casino that had a losing day shut down, there would be no casinos. If every loser never had a winning day, there would be very few losers. We have to use statistics to determine whether we truly have a positive expectation.
To that end we have written a program to generate statistics for each our picks for each week. We will begin reporting this along with our weekly picks beginning in Week 4, we have back-calculated it for weeks 1 – 3. Our results are within expectations, though indication is that we are slightly on the lucky side of expectation value for the season.
Week 1 – 9 units bet – 55.6% expectation – 105.7% ROI (10% juice) – 38.7% odds of losing for the week – Actual return 67% (probability of 67%+ was 40.2%)
Week 2 – 13 units bet – 58.4% expectation – 110.9% ROI (10% juice) – 33.7% odds of losing for the week – Actual return 69% (probability of 69%+ was 38.6%)
Week 3 – 15 units bet – 58.1% expectation – 110.4% ROI (10% juice) – 33.7% odds of losing for the week – Actual return not yet determined (55% so far).
By the way, there is a place for gambling for entertainment. That is not our focus here. If it seems like we’re taking the fun out of gaming, that’s intentional. When we go to the horse track (for now) we make small bets just for fun expecting to lose and grateful when we win. When we spend hours or days coming up with picks that often go against our better intuition, our intention is to win money (and to help our readers win money). This is not about hunches, fun, entertainment, etc. If that’s what you want just bet on your favorite teams.
We’ve run our statistical models for our week three 2009 nfl picks. There is good news and bad news. We only found three bet-worthy lines, but we added our over-under picks (five were statistically distinct from the Vegas Odds). Over-all we place fifteen bet units on 8 bets. Carolina with the points and Under on the Philadelphia vs KC game are the best bets.
