Twitter Feed

I can't imagine I am going to update this often before football season, but I added a twitter feed. I already don't like it.

Future Win Totals Thinking Revisited

I've got time to put the full record update up since games don't start again until Thursday, so I figured I would add to this post from last week.

Since I figured out the expected wins of each team from the totals posted at BetUS, all I needed was a standard deviation to come up with a distribution of the probability of each team having a particular number of wins. I did this by using last year's totals and actual results to come up with a root mean squared error estimate of the standard deviation, which was around 2.16.

I'll let Jonny analyze each particular team, but I came up with an unexpected and almost assuredly erroneous result. The numbers show the expected number of teams that should go undefeated is 4.27.

There a couple of reasons that immediately spring to mind why this number is wrong. First off, my methodology could be wrong. I used a normal distribution to estimate probabilties for discrete data. While not terrible, it's still not good (for math geeks, this is analogous to using the trapezoidal rule for estimating integrals).

Also, it may not be a good idea to use a symmetric, normal distribution for totals that are 10, 11, or 12, since the tails of a normal distribution go to infinity and you are buttressing against the total possible number of wins.

Another reason would be that last year's RMSE is not representative of the true standard deviation of the distribution. The last thing that comes to mind is the books shading the lines high for the expected good teams, expecting to take a lot of over action.

To use a further example, last year Moneyline, when he was still running a blog, estimated USC's chances of going undefeated at 14.8% and their chances of one loss at 32.4%. Using this methodology, 2008 USC had a 31.3% chance of going undefeated and a 17.7% chance of one loss. Obviously, Moneyline's numbers are way closer to reality than these are.

There is quite a bit of room for improvement here, I'm just not sure where.

People Google Weird Things

Why have four different people hit my blog in the last 24 hours from googling "Stallone's namesake?" I mean, on what planet does someone need to search for that?

Football Predictors

I'm finally feeling like putting all of my academic statistical knowledge to use. With the help of some other people in the contrarian community, I'm going to try a first stab at handicapping this summer (yes, summer). Before I do all the necessary coding for the stats stuff, I need to figure out what the best predictors are for success in college, pros, and/or both so the data can be acquired and manipulated.

My initial list looks like this: points scored, points allowed, yards gained, yards allowed, and interceptions thrown. The first four should be pretty obvious. The last one is the proxy for turnovers. Clearly, turnovers affect a football game's outcome, but other research has shown that fumbles are fairly random. However, interceptions depend mostly on the quality of the quarterback and are predictable from week-to-week (as any Penn State fan from 2006-7 can attest).



If you wanted to predict outcomes of games, what statistics would use and why would they be better than the ones I'm looking at trying? Also, in the comments, feel free to make fun of me for even bothering to attempt a project that will surely fail.

TheFiancee is a Degenerate

At least I'm engaged to the right person. TheFiancee went to Atlantic City today, counted cards in blackjack, and came home +$370. She's doing way better than I am sports gambling.

Mine That Bird?! Really??

I was nearly even before the Derby, but ended up down about $150 for the day. Here is what I had:

$5 Exacta Box - 5, 6, 15 ($30)
$1 Tri Box - 5, 6, 15 / 5, 6, 11, 12, 15, 16 / 5, 6, 11, 12, 15, 16, 17 ($75)

That was going to be it until my buddy freaked me out about the UAE horse, so I added:

$2 Exacta Box - 19 / 5, 6, 15 and 5, 6, 15 / 19 ($12)
$4 Win/Place - 19 ($8)

Obviously, none of those hit. Which was neat. It was worse for my buddy. He crossed off a trifecta horse for Race 9 at Churchill that ended up hitting and costing him $1800. This was also the first year he didn't hit the winner in 15 years, but he bets like 3/4 of the field, so it's not like it was any sort of feat.

I'm Bored

I have no desire whatsoever to touch the rest of this NCAA Tournament. Sparty is playable today, if you haven't had your will broken. On the other hand, I'd like to have some action before Tuesday's NIT/CBI games tip off.

Plays
5p Nashville +290 1x to win 2.9x
8p Colorado +236 1x to win 2.36x
Pass: Minnesota

Yes, that's right, hockey bets. I've been marginally following the league this year and know that Detroit is really good and Colorado is really bad. There isn't really much else you need to know about these wagers.

Streak for the Cash
220p Michigan State/Louisville U139.5 points
5p North Carolina/Oklahoma O163.5 points
8p New Orleans vs. San Antonio
Current Streak: 1

This total seems low given the way the rest of the second weekend has turned out. Of course, the Big Ten doesn't play offense (LULZ!). The line is down to 137.5 at Pinny, so whatever.

I've got no idea what to pick for the late hoops game. I'm going over on the assumption that the Sooners will put up some points, I guess, but 163.5 is a freaking lot.

Another coin flip upcoming. The split at Pinny is -115/+105 in favor of the Spurs. I'm taking the Hornets just so I can keep my contrarian card for the day.

Good luck today.

Looking at Time Series, Part 2

In part one, I looked at time series analyses of my 2008-09 college gambling results and it wasn't pretty.

Tonight, I'm going to look at 2007-08, since most of my contrarian thoughts were forged during that period and, conveniently, I have statistics for them.

The graph from the 07-08 NCAAB season is shown below. It has an uptrend (that'd be nice), so I'm probably going to have to look at the first differences.



Now here is an interesting result. At first glance, it almost looks like a random walk again.

Type Coef SE Coef T P
AR 1 0.9881 0.0156 63.18 0.000
Constant 0.2549 0.1641 1.55 0.122
Mean 21.34 13.74

But, remember, I said I have to look at the first differences because of the upward trend. Here is the time series analysis on the first differences.

Type Coef SE Coef T P
AR 1 0.1142 0.0631 1.81 0.071
Constant 0.1444 0.1532 0.94 0.347

The constant term is not statistically significant, which kind of sucks. If it were significant, it would indicate skill. Regardless, the AR1 coefficient is marginally significant. I'm not sure if that really means there was predictive ability in 2007-08, but at least it shows statistically that I was more likely to win the game after a win (and lose after a loss).

The college football season from 2007-08 also had a positive trend (below), so a similar analysis will need to be completed on the first differences.



For the 2007-08 NCAAF data, the trend was so severe, the time series program died before converging, so we'll have to go straight to the first differences.

Type Coef SE Coef T P
AR 1 0.0203 0.0838 0.24 0.809
Constant 0.4808 0.2598 1.85 0.066

This is what I wanted to see all along. The AR1 term for the first differences is very close to zero and the constant term is positive and statistically significant. Basically, that is saying that each bet was totally independent and had an expected outcome of 0.5x. Statistical skill, finally.

For completeness, I suppose I should look at 2008-09 college football from Week 3 forward. Intelligently, I didn't even play the NCAAF in Week 2.



Not much change from the original.

Type Coef SE Coef T P
AR 1 0.0095 0.0856 0.11 0.912
Constant 0.5586 0.2693 2.07 0.040

No real change in time series statistics either. Hooray. One good season in the last six.

Clearly, I think the results show I had some skill in 2007-08. Whether that is attributed to blind luck, a different gambling market, or strategy changes causing a decline in my abilities, I'm not sure. Since it seems that contrarians in general have records that are worse this year overall compared to last, I'm hopeful, but not convinced, that 2008-09 is just a hiccup on the path to success.

Looking at Time Series Analysis of am19psu's Results

Well, I went ahead and downloaded Minitab so I could do some ARIMA modeling of my data. I have to admit, the preliminary results are a little disheartening. One thing to keep in mind is that the strategy is ever-evolving, so the results here may not have come from the same distribution, but I doubt that is playing much of a factor.

As you recall, the figure below shows my results through March 1st for 2008-09 basketball season.



I used disheartening above because when I did the time series analysis I came up with this:

Type Coef SECoef T P
AR 1 0.9712 0.0158 61.46 0.000
Constant 0.0645 0.1481 0.44 0.664
Mean 2.235 5.133

Basically, what that means is that my results are a random walk. The AR1 coefficient is the coefficient of the serial correlation term with p-value 0.000 and the constant is not significant (with p-value 0.664). Note that the coefficient is near 1. If it were exactly 1, it would be a true random walk. This pattern is one that is destined to fail over time. It's even been called gambler's ruin.

This result has many names: the level-crossing phenomenon, recurrence or the gambler's ruin. The reason for the last name is as follows: if you are a gambler with a finite amount of money playing a fair game against a bank with an infinite amount of money, you will surely lose.

And in general, we're not even playing a fair game because of juice.

This graph shows the results from 2008-09 college football.



The time series analysis for this sucks even worse.

Type Coef SE Coef T P
AR 1 0.9382 0.0300 31.26 0.000
Constant -0.8430 0.1800 -4.68 0.000
Mean -13.636 2.912

In this particular example, the AR1 term is a bit farther away from 1, meaning it is likely not a random walk, but the constant term is negative. That means I am a loser when it comes to college football, at least in 2008-09. Not that I had any inclination of that from the sidebar.

The next figure shows my NCAAF results with the first two weeks left out. One of the things that I've considered is that contrarian gambling early in the season is not profitable. ML has told me stories about Squeeky cleaning up in college football early in a season, 2004 or 2005, I believe. However, I have not had any luck whatsoever. This graph doesn't necessarily support that hypothesis, but it is fair to say that I never got out of the hole I dug myself the first two weeks.



These results are much more indicative of what I want to see. With a coefficient of AR1 around 0.84 and positive constant, I am showing NCAAF to be profitable for 2008-09 after Week 2.

Type Coef SE Coef T P
AR 1 0.8451 0.0445 18.99 0.000
Constant 1.0931 0.1816 6.02 0.000
Mean 7.056 1.173

Whew. At least, debatably, I'm not completely retarded. That said, when I look at the first differences, the p-value of AR1 is not statistically significant, so it is still possible that results are a random walk.

I'm not even going to look at the last two NFL seasons, since I readily admit I have no skill in that sport.

I'm not entirely sure what to make of these results, other than I have not been gambling skillfully in 2008-09. More questions and suggestions are always welcome in the comments.

In part two of this post, which will run Friday at 6PM, I'll look into my 2007-08 results.

Leans 2/27

I'm hesitant to even think about putting a Metro Atlantic team here. However, most people remember the Siena Saints winning a first round game last year, and if you've been reading different bracket projections over the last few weeks, there is weak support for Siena as an at large team. Taking those grains of salt, I lean toward

7p Niagara -2.5 vs. Siena

I have no idea whether the Purple Eagle will be anti-public and I put the probability of actually playing the game under 30%, but I just wanted to get it out there.

Other than that, nothing but a bunch of Metro Atlantic teams you've never heard of or Ivy League games that I know nothing about.