Posts Tagged tournament
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.
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).
|Ohio St||10.54||Ohio St||3.55|
Mr. Pomeroy “likes” the Big Ten, Pinnacle doesn’t.
|S Dakota St.||0.8||0.03||0.41||0.29||0.39||-0.26|
In Microsoft Excel.
Cells colored in blue are customize-able. Each team is rated in sheet “DATA” with Pythagorean Winning Percentage using average line adjusted to schedule and average total. The resultant rating is this winning percentage * 100. Feel free to insert whatever rating you prefer from 1-100. Sheets “BRACKET32″ and “BRACKET16″ are the Simulators labeled appropriately.
In Round 1, there is a drop down box for each matchup to select the teams/seedings. Additionally, there are seeding presets using the rankings per the button labels. Just click the button to set the seedings
There are three customize-able boxes in each BRACKET sheet labeled STD, ITERATIONS, and HFA
The Bracket shows the result of each single simulation, the table next to the brackets demonstrate the aggregate results of the simulation. I did this for the sake of speed. Calculating the cumulative winners then placing them in the respective fields in the brackets takes way too long in excel. I am assuming speed is preferable to how the brackets are displayed.
Home Field Advantage is used up to the final four in “BRACKET32″, and includes the final four in “BRACKET16″, just set HFA to 0 to remove HFA.
Depth in these tournament games is a major non-component when determining how far a team advances. March Madness breeds longer TV timeouts, more deadball situations, and other elements that would induce stoppage that do not normally occur during the regular season. By degrees as teams move on to the next round, a coach is inclined to shorten his bench minute distribution to get the best possible five out on the court throughout the course of the game. Its a myth that depth in the tournament is a significant factor. To arrive at this resolve, I’ve aggregated a set of data showing appropriation of a team’s bench minutes. Here are the percentage of regular season bench minutes extracted from kenpom of the remaining final four:
A quick survey of the table betrays the insignificance of depth, not only in this tournament, but in the regular season. Not one remaining team is ranked inside the top 140. The next table shows how coaches have even further hardened the barrier between playing and sitting the bench.
The average is only slightly below that of the regular season table, however when taking into account Michigan State being severely encumbered by injuries to key players, and West Virginia having a ratio of bench/starter minutes approaching 50/50 induced by a complete annihilation of Morgan State in round 1, the tournament numbers are in practice lower than what is actually calculated. And the next graph will show how as the tournament progresses, the allocation of bench minutes tightens considerably:
x = Round #
y = percentage of bench minutes
The reduction of the possible contribution by way of bench is logarithmic in relationship, a much more glaring signifier of the significance of the starting lineup than if it were a linear relationship. A linear relationship would still show how teams shrink their bench from round to round, but with a lower reliant variance. The coefficient of determination isn’t used here to actually make an analytical prediction on the percentage of bench minutes each team will exercise in the final four, but merely for a quantitative comparison and proof.
Because Butler is a 5 seed, they have had a relatively tougher path to navigate. Despite close and hard fought games throughout, their bench minutes have slightly declined from round to round. For Michigan State, due to injuries as mentioned before, Izzo has had to introduce some innovative schematics into his bench allocation philosophy. Duke and West Virginia would be expected to have the sharpest reduction of playing time from the bench, because of their seed. WVU has a very high coefficient of determination, meaning the regression line is very close to matching the actual set of data points. What that does is indicate a consequential decline in bench minutes from round to round. As pointed out before, though reiterating with varied rhetoric, they completely obliterated Morgan State leading to garbage minutes commandeered by bench warmers. Regressing WVU’s round one game to a more manageable number, their coefficient of determination (R2) would be very similar to Duke’s or Michigan State’s.
Now having deduced from the table and graph that essentially the starting five has a more measurable impact in the tournament from the bench corollary, what is the best way to figure out which team has the best or most effective set of starters?
Efficiency, Eff – The NBA Efficiency Model, which attempts to reward key “good” stats and punish “bad” stats, all in one single statistical measurement. Formula: ((Pts + TReb + A + Stl + Blk) – ((FGA – FGM) + (FTA – FTM) + TO))
I’ve discovered after observing some of the players’ efficiency data, that each of the remaining four teams appear to have three key players, and the rest play supporting roles in some form, often times the four and five starters are juggled with various bench players for starting positions apropos to the dynamics of an MLB team’s starting rotation.
Top three for each team:
|Da’Sean Butler||16.2||Jon Scheyer||17.2|
|Devin Ebanks||14.4||Kyle Singler||15.9|
|Kevin Jones||14.8||Nolan Smith||13.5|
|Draymond Green||15||Gordon Haywood||17|
|Raymar Morgan||13.1||Shelvin Mack||12.9|
|Kalin Lucas||12.2||Matt Howard||11.5|
West Virginia and Duke are virtually even in terms of top-level basketball acuity measured by efficiency, and the ratings basically show as much. BBState currently has Duke 2nd compared to WVU’s 4 ranking, and Kenpom has the spread at a modest five points favoring Duke, which says a lot about the Mountaineers considering this year’s Duke team is the greatest in history. Butler, higher rated in both BBState and Kenpom, also has the top three advantage even with a healthy Kalin Lucas for Michigan State. After Lucas for the Spartans, two players, Durrell Summers and Devlin Roe, have an efficiency rating of 9.3 and 9.2 respectively. Durrell Summers has performed admirably up to this point in filling the relative void left by Lucas. He’s been their proverbial spark, though injuries will likely catch up to Spartans, and apparently Butler is just better.
Same deal as before, just public numbers via screenshots from different sources (click to enlarge).
Public fades have fared pretty well this week. Both games today would merit public fade status, as well as point advantages from kenpom. The Duke game is interesting because of location and betting trends. Fans love to hate Duke, hence the public dog status held by Baylor warranted by the percentages. The oddsmakers denoted Baylor’s proximity to Houston compared to Duke’s to be worth a 1-1.5 points, which is reasonable enough. Though since the opener 4.5 the line has creeped up to 5. Kenpom has the spread at 6. Formulating all the aforementioned conditions into one sensible sentence, Duke is a #1 seed being heavily bet against by the public despite having the point advantage between the line and kenpom, with sharp money moving the line up a half point. Good luck Baylor bettors.
I would lean Duke, obviously, just based on the public numbers and the raw data, however I really don’t feel compelled to make a wager on this particular game in order to allow my future bets to take form.