Comparing Rating Systems For NCAA Tournament
Posted by Rufio Magillicutty in Betting, kenpom, NCAAB, Pinnacle, tournament on March 15, 2012
The images represent how each rating system projects the NCAAB Tournament. The higher rated team was selected for each matchup. For the bracket labeled “PINNY”, the future odds from Pinnacle were used to assess team-by-team comparison. Click on the image for full-size view.
- KENPOM
- BBSTATE
- PINNY
- SAGARIN
- RPI
NCAA Tourney KP vs Pinny
Posted by Rufio Magillicutty in Betting, NCAAB, Pinnacle on March 15, 2012
Same thing as conference tournaments. SEC Field hit at 3/1 odds, the other four lost. A brief survey of a hypothetical bankroll outcome demonstrated the prodigious and frightening force of the Kelly Criterion and all the emotional turmoil likely to beget its constituency. Flat bettors would have come away in the negative, but with an air of optimism and satisfaction having lingered for hitting a future.
KenPom’s LOG5 predictions are here. If you don’t know what that means, to wit:
LOG5 = (a – a * b)/(a + b – 2 * a * b)
“a” and “b” here are winning percentages. KenPom uses his pythagorean winning percentages calculated by PPP and tempo rather than just points scored for and against, with an exponent of around 12.
(Numbers in each cell represent percentages sans the non-obligatory “%” symbol).
| TOP 5 | |||
| REGION | CHAMP | ||
| Ohio St | 10.54 | Ohio St | 3.55 |
| Mich St | 7.64 | Wisconsin | 2.24 |
| Wisconsin | 6.97 | Mich St | 1.98 |
| Kansas | 6.8 | Kansas | 1.74 |
| Indiana | 3.38 | Indiana | 0.67 |
Mr. Pomeroy “likes” the Big Ten, Pinnacle doesn’t.
| SOUTH | ||||||
| KP | PINNY | KP-P | ||||
| TEAM | REGION | CHAMP | REGION | CHAMP | REGION | CHAMP |
| Kentucky | 47.9 | 19.7 | 47.4 | 27.78 | 0.5 | -8.08 |
| Wichita St | 11.8 | 2.6 | 8.43 | 2.32 | 3.37 | 0.28 |
| Indiana | 9.2 | 1.7 | 5.82 | 1.03 | 3.38 | 0.67 |
| Baylor | 10.9 | 1.7 | 12.08 | 2.82 | -1.18 | -1.12 |
| Duke | 9.5 | 1.7 | 12.08 | 4.8 | -2.58 | -3.1 |
| UNLV | 3 | 0.2 | 3.51 | 0.73 | -0.51 | -0.53 |
| Iowa St. | 1.7 | 0.1 | 1.31 | 0.42 | 0.39 | -0.32 |
| Notre Dame | 1.9 | 0.1 | 1.96 | 0.44 | -0.06 | -0.34 |
| Uconn | 0.9 | 0.06 | 2.58 | 1.07 | -1.68 | -1.01 |
| Xavier | 0.09 | 0.04 | 1.32 | 0.43 | -1.23 | -0.39 |
| S Dakota St. | 0.8 | 0.03 | 0.41 | 0.29 | 0.39 | -0.26 |
| VCU | 0.5 | 0.02 | 0.79 | 0.29 | -0.29 | -0.27 |
| Colorado | 0.4 | 0.01 | 0.67 | 0.29 | -0.27 | -0.28 |
| NMSU | 0.3 | 0.01 | 0.41 | 0.35 | -0.11 | -0.34 |
| Lehigh | 0.3 | 0.007 | 0.4 | 0.21 | -0.1 | -0.203 |
| WKY | 0.001 | 0.82 | 0.32 | -0.819 | -0.32 | |
| MIDWEST | ||||||
| KP | PINNY | KP-P | ||||
| TEAM | REGION | CHAMP | REGION | CHAMP | REGION | CHAMP |
| UNC | 28.5 | 6.6 | 32.95 | 13.64 | -4.45 | -7.04 |
| Kansas | 33.7 | 9.1 | 26.9 | 7.36 | 6.8 | 1.74 |
| Gtown | 9.7 | 1.4 | 7.31 | 1.45 | 2.39 | -0.05 |
| Michigan | 5.7 | 0.5 | 4.57 | 0.88 | 1.13 | -0.38 |
| Temple | 2.3 | 0.1 | 3.92 | 0.64 | -1.62 | -0.54 |
| SDSU | 0.9 | 0.03 | 2.65 | 0.52 | -1.75 | -0.49 |
| St. Mary’s | 1.2 | 0.05 | 2.65 | 0.59 | -1.45 | -0.54 |
| Creighton | 2 | 0.1 | 1.61 | 0.43 | 0.39 | -0.33 |
| Alabama | 3.1 | 0.2 | 2.04 | 0.57 | 1.06 | -0.37 |
| Purdue | 3.9 | 0.3 | 3.92 | 0.73 | -0.02 | -0.43 |
| NC State | 1.5 | 0.07 | 4.57 | 0.73 | -3.07 | -0.66 |
| USF | 0.3 | 0.008 | 0.81 | 0.66 | -0.51 | -0.652 |
| Ohio | 0.5 | 0.01 | 0.81 | 0.29 | -0.31 | -0.28 |
| Belmont | 4 | 0.03 | 3.92 | 0.85 | 0.08 | -0.82 |
| Detroit | 0.07 | 0.54 | 0.21 | -0.47 | -0.21 | |
| Vermont | 0.03 | 0.84 | 0.39 | -0.81 | -0.39 | |
| WEST | ||||||
| KP | PINNY | KP-P | ||||
| TEAM | REGION | CHAMP | REGION | CHAMP | REGION | CHAMP |
| Mich St | 35.2 | 12.4 | 27.56 | 10.42 | 7.64 | 1.98 |
| Missouri | 23.1 | 5.3 | 22.63 | 8.31 | 0.47 | -3.01 |
| Memphis | 8.2 | 1.7 | 5.67 | 1.61 | 2.53 | 0.09 |
| New Mexico | 7.1 | 1 | 7.84 | 1.33 | -0.74 | -0.33 |
| Marquette | 7.5 | 0.9 | 9 | 2.34 | -1.5 | -1.44 |
| Loserville | 4.7 | 0.5 | 9.08 | 2.61 | -4.38 | -2.11 |
| Florida | 4.4 | 0.5 | 3.97 | 0.8 | 0.43 | -0.3 |
| St. Louis | 3.4 | 0.5 | 2.2 | 0.57 | 1.2 | -0.07 |
| Virginia | 2.5 | 0.2 | 1.78 | 0.43 | 0.72 | -0.23 |
| Murray St. | 1.4 | 0.07 | 3.05 | 0.73 | -1.65 | -0.66 |
| LBSU | 1 | 0.06 | 1.3 | 0.29 | -0.3 | -0.23 |
| BYU | 0.5 | 0.02 | 3.91 | 0.97 | -3.41 | -0.95 |
| Davidson | 0.3 | 0.009 | 0.71 | 0.29 | -0.41 | -0.281 |
| Colorado St. | 0.4 | 0.008 | 0.52 | 0.29 | -0.12 | -0.282 |
| LIU | 0.003 | 0.39 | 0.17 | -0.387 | -0.17 | |
| Norfolk St | 0.0001 | 0.39 | 0.21 | -0.3899 | -0.21 | |
| EAST | ||||||
| KP | PINNY | KP-P | ||||
| TEAM | REGION | CHAMP | REGION | CHAMP | REGION | CHAMP |
| Syracuse | 17.5 | 4.4 | 18.22 | 5.72 | -0.72 | -1.32 |
| Ohio St | 45.9 | 19.3 | 35.36 | 15.75 | 10.54 | 3.55 |
| FSU | 3.9 | 0.5 | 9.29 | 4.08 | -5.39 | -3.58 |
| Wisconsin | 16.2 | 4.2 | 9.23 | 1.96 | 6.97 | 2.24 |
| Vanderbilt | 4.9 | 0.8 | 7.92 | 2.81 | -3.02 | -2.01 |
| Cincinnati | 1.8 | 0.2 | 4.39 | 1.03 | -2.59 | -0.83 |
| Gonzaga | 1.7 | 0.1 | 2.4 | 0.59 | -0.7 | -0.49 |
| Kansas St | 3.4 | 0.4 | 4.39 | 0.98 | -0.99 | -0.58 |
| S. Miss | 0.2 | 0.006 | 0.98 | 0.34 | -0.78 | -0.334 |
| WVU | 0.8 | 0.05 | 2.4 | 0.59 | -1.6 | -0.54 |
| Texas | 2.3 | 0.2 | 2.2 | 0.52 | 0.1 | -0.32 |
| Harvard | 0.7 | 0.04 | 1.11 | 0.29 | -0.41 | -0.25 |
| Montana | 0.09 | 0.002 | 0.79 | 0.29 | -0.7 | -0.288 |
| St. Bona | 0.6 | 0.03 | 0.53 | 0.29 | 0.07 | -0.26 |
| Loyola | 0.02 | 0.4 | 0.17 | -0.38 | -0.17 | |
| UNC-Ashe | 0.03 | 0.4 | 0.17 | -0.37 | -0.17 | |
NCAAB Conference Tourney’s: Pinny v KenPom
Posted by Rufio Magillicutty in kenpom, NCAAB, Pinnacle on March 6, 2012
KenPom LOG5′s here
Top 5 Value:
Kansas
Indiana
Syracuse
Ohio State
SEC Field
| ACC Tourney | |||
| Team | Pinny | KP | KP-P |
| UNC | 53.52% | 52.20% | -0.0132 |
| Duke | 21.30% | 19.80% | -0.0150 |
| FSU | 10.39% | 9.90% | -0.0049 |
| UVA | 6.65% | 10.40% | 0.0375 |
| NC State | 3.44% | 2.40% | -0.0104 |
| Miami | 2.48% | 3.70% | 0.0122 |
| Field | 2.22% | 1.60% | -0.0062 |
| PAC 12 Tourney | |||
| Team | Pinny | KP | KP-P |
| California | 30.29% | 32.30% | 0.0201 |
| Oregon | 15.94% | 12.20% | -0.0374 |
| UCLA | 14.82% | 14.90% | 0.0008 |
| Washington | 14.76% | 12.20% | -0.0256 |
| Arizona | 9.47% | 12.70% | 0.0323 |
| Stanford | 5.98% | 8.00% | 0.0202 |
| Colorado | 3.93% | 3.90% | -0.0003 |
| Oregon St. | 2.87% | 2.80% | -0.0007 |
| Field | 1.94% | 1.00% | -0.0094 |
| SEC Tourney | |||
| Team | Pinny | KP | KP-P |
| Kentucky | 72.63% | 68.70% | -0.0393 |
| Field | 27.37% | 31.30% | 0.0393 |
| Big East Tourney | |||
| Team | Pinny | KP | KP-P |
| Syracuse | 33.02% | 39.50% | 0.0648 |
| Marquette | 18.42% | 20.10% | 0.0168 |
| Georgetown | 13.81% | 15.10% | 0.0129 |
| Notre Dame | 7.21% | 7.90% | 0.0069 |
| Cincy | 6.94% | 5.60% | -0.0134 |
| Loserville | 6.94% | 5.50% | -0.0144 |
| USF | 3.53% | 1.50% | -0.0203 |
| WVU | 3.52% | 1.90% | -0.0162 |
| Uconn | 2.78% | 1.40% | -0.0138 |
| Seton Hall | 1.27% | 0.70% | -0.0057 |
| Pitt | 1.08% | 0.20% | -0.0088 |
| St. John’s | 0.37% | 0.08% | -0.0029 |
| Field | 1.10% | 0.52% | -0.0058 |
| Big Ten Tourney | |||
| Team | Pinny | KP | KP-P |
| Ohio State | 32.29% | 37.20% | 0.0491 |
| Michigan St. | 23.34% | 27.20% | 0.0386 |
| Michigan | 15.96% | 5.10% | -0.1086 |
| Wisconsin | 10.48% | 10.40% | -0.0008 |
| Indiana | 9.74% | 16.50% | 0.0676 |
| Purdue | 3.73% | 2.50% | -0.0123 |
| Nwestern | 2.17% | 0.50% | -0.0167 |
| Illinois | 0.79% | 0.20% | -0.0059 |
| Field | 1.51% | 0.40% | -0.0111 |
| Big 12 Tourney | |||
| Team | Pinny | KP | KP-P |
| Kansas | 39.33% | 54.60% | 0.1527 |
| Missouri | 32.93% | 23.00% | -0.0993 |
| Baylor | 9.43% | 7.30% | -0.0213 |
| Kansas St. | 6.52% | 5.00% | -0.0152 |
| Iowa St | 5.97% | 4.50% | -0.0147 |
| Texas | 4.96% | 5.00% | 0.0004 |
| Oklahoma St | 0.57% | 0.40% | -0.0017 |
| Field | 0.29% | 0.20% | -0.0009 |
“Time need not end” for a degenerate
Posted by Rufio Magillicutty in Kelly, Probability, Science on January 11, 2012
Apparently, time is on my side. Which is funny if you’ve followed my twitter fades the last century. (Obviously time is relative, what may seem like two months to you is almost certainly an eternity to tweeters of guaranteed fades, c’est moi, FML, c’est moi.)
It all started with this thought experiment. In a back room in a Las Vegas casino, you are handed a fair coin to flip. You will not be allowed to see the outcome, and the moment the coin lands you will fall into a deep sleep. If the coin lands heads up, the dealer will wake you 1 minute later; tails, in 1 hour. Upon waking, you will have no idea how long you have just slept.
The dealer smiles: would you like to bet on heads or tails? Knowing it’s a fair coin, you assume your odds are 50/50, so you choose tails. But the house has an advantage. The dealer knows you will almost certainly lose, because she is factoring in something you haven’t: that we live in a multiverse.
Oh god.
In any infinite multiverse, everything that can happen, will happen – an infinite number of times…How can we say that anything is more or less probable than anything else?
One procedure physicists are fond of is to draw a cut-off at some finite time, count up the number of events – say, heads and tails – that occur in the multiverse before the cut-off time, and use that as a representative sample.
It seems reasonable, but when tackling the casino experiment, something strange happens. Wherever the cut-off is drawn, it slices through some of the gamblers’ naps, making it appear as if those gamblers simply never woke up. The longer the nap, the more likely it is to be cut off, so if you do awaken, it’s more likely that you have taken a shorter nap – that is, that you flipped heads. So even though the odds seemed to be 50/50 when the coins were first flipped, heads becomes more probable than tails once you and the other gamblers wake up.
Somewhere deep down this is what J. L. Kelly, Jr. had in mind. Accidental prescience? I knew it.
Upon waking, you have new information: you know that time didn’t end. That now means it is more likely that you only slept for a minute than for an hour. After all, time could end at any minute, and an hour has an extra 59 of those to spare. Heads wins.
Ultimately, younger universes are more numerous than older universes, thus if I interpret each possible side as if its occurring primarily in younger universes, my probability increases at the rate proportional to y/u, where y is the number of younger universes, and u the number of older universes. I’ve figured it out, guys.
New Scientist. August 13, 2011
Coin Toss Flat v Kelly Simulation
Posted by Rufio Magillicutty in Betting, Excel, Expected Value on October 31, 2011
*Repost from too long ago.
I created a coin toss simulator (at the bottom) to compare two different wagering methodologies. But first I’m going to ramble.
Statistics, streaks, can be deceiving, and frequently this feature of statistics is abused. Services and handicappers that sell picks often either just plain lie about their betting performance, or use statistics over a small subset of games that only act as data in a larger sample size.
You can take any record of picks over a certain time frame, and do a linear extrapolation into future expectation and growth, serving to highlight excellence in picking games. I went 0-7 in college football two weeks ago, if I use that and do a linear extrapolation through the next 50 years I’ll be losing money at the speed of light expanding infinitely up into the outer regions of space.
Rarely does anybody have constant growth in bankroll, as the only thing absolute is that there will be variation and fluctuation in winners and losers. But calculating expected growth is a practical application imposing upon a system of random performance. If a bettor is betting 25% of her bankroll on every play, all it takes is one 0-4 sequence of wagers to lose everything.
Expected Value, to wit:

An example would be a standard -110 wager betting $100.

Expected growth is typically demonstrated in terms of bankroll growth and bankroll shrinkage. Therefore X (Money wagered) would be expressed as percentage of bankroll. For the above example, a bankroll of $10,000 at 1% per game equates to $100. The formula for expected growth:
D = Decimal Odds
p = Win Probability

The Kelly Criterion accounts for such scenarios, and finds the most optimal distribution of bankroll to apply for each wager, given some designated edge. Kelly is a formula to maximize expected growth, while minimizing risk.
Conversely, flat betting (betting the same amount on each game) defines the edge as immaterial, and embraces randomness and humility. With either style of managing bankroll, allowing one to stick around long enough to hit a long shot is eminently assumed, in my opinion of course.
Again, Kelly essentially finds the optimal wager for an event that maximizes E(G), from above. Given an edge, however measured, the “Kelly Stake” can now be calculated as a percentage of bankroll (of course if there is no edge there is no wager).

Conveniently at even odds the stake is equal to the edge. One thing to keep in mind, Kelly intended each particular stake to relate to each particular moment of current bankroll. Meaning a $1 winner with a bankroll of $100 now increases the bankroll to $101 and therefore changes the value of the next wager given similar parameters of the wager previous.
Kelly is very aggressive, there is no doubting that, which is why fractional Kelly is more common, in my experience anyways. I don’t use the Kelly criterion because there is seldom a time where a calculated edge has any actual meaning, and I’m not arrogant enough to think I am smarter than Vegas. Therefore I bet frivolously and arbitrarily.
The simulator is below, and uses “static” Kelly Criterion, if you will, to compare to flat unit stakes. Enter numbers in the blue boxes then click simulate. Its a zip file. Two excel macro files inside, 0ne is a side by side comparison, the other just allows for the simulation of the specified methodology.





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