Anatomy of the March Madness Upset Part 3 – 2017 Picks

For those who have been following along, we have been analyzing the past 15 years of March Madness upsets to try to figure out who are the best candidates to bust brackets.  Catch up on part 1 here  and part 2 here.  In quick recap we saw some common patterns across histories college basketball underdogs, particularly that most of these teams excelled in at least one of the following areas: 2 point percentage, 3 point percentage, defensive turnover percentage, offensive rebounding percentage, or has an elite adjusted defensive efficiency (points per possession allowed adjusted for competition).  Why did we go through all of that work?  To better select candidates for 2017’s tournament, which is quickly approaching.

Lets get right to it, with my March Madness analysis.

Lower Seed Higher Seed Seed AdjEM Diff 2Pt% 3Pt% Def TO% Off Reb% AdjDefEff Synopsis Money Line
Troy Duke 15-2 22.42 50.8 35.5 17.7 30.6 107.3 3000
North Dakota Arizona 15-2 23.06 51.6 28.4 20 26.2 103.2 1427
Jacksonville St. Louisville 15-2 26.14 50.9 36.7 16.8 31.5 104.8 3600
Northern Kentucky Kentucky 15-2 25.93 51.5 34.2 20.1 36.3 109.5 3310
New Mexico St. Baylor 14-3 17.79 54.5 33.4 19.2 36 102.7 candidate 616
Florida Gulf Coast Florida St. 14-3 17.73 55.5 34 17.3 34.5 104.5 candidate 559
Iona Oregon 14-3 19.83 49.3 39.8 18.1 27 106.2 candidate 1050
Kent St. UCLA 14-3 21.22 48.6 31.4 19.3 38.1 102.7 1750
East Tennessee State Florida 13-4 14.9 55.4 38.2 22 30 96.5 candidate 490
Bucknell West Virginia 13-4 18.08 54.6 37.7 19.8 26.8 100.3 candidate 1000
Vermont Purdue 13-4 12.34 55.6 36.7 19.7 30.1 98.6 candidate 358
Winthrop Butler 13-4 16.28 51.2 38 18.7 27 101.8 candidate 500
UNC Wilmington Virginia 12-5 14.28 56.1 36 20.4 32.5 105.4 candidate 353
Princeton Notre Dame 12-5 7.51 50.4 38.1 20.6 24.2 96.9 candidate 253
Nevada Iowa St. 12-5 9.77 49.3 38.5 15.9 30.6 101.2 candidate 236
Middle Tennessee Minnesota 12-5 1.94 53.8 37 20.2 30.2 97 -115
Providence/USC SMU 11-6
Xavier Maryland 11-6 -0.5 52 34 17.8 35.2 99.5 113
Rhode Island Creighton 11-6 3.97 50.5 34 19.5 33.1 95 -102
Kansas St./Wake Forest Cincinnati 11-6

This years crop of 11-15 seeds are particularly interesting to me.  Lets break it down.  First off the 15 seeds, I am not in love with any of these potential upsets this year, all have below average defenses and don’t standout in any offensive categories.  I will be skipping betting these match-ups this year.

Lets talk 11-6 matchups.  We won’t know two of these until the play-in games.  I like both USC and Wake Forest to take care of business and secure spots in the tourney, but I skipped my analysis for these games as they really aren’t upsets, they are larger named schools who see similar quality of opponents all year long.

The classic 12-5 is the first place people typically look for “upsets”.  My usual quarrels with the 11-6 not being upsets have crept into play this year into the 12-5 match-ups.  Particularly Middle Tennessee State, it is a considered a pick’em by Vegas.  The other 3 match-ups are your more traditional 12-5’s, where we typically see an upset 25% of the time.   That being said, I like all of the 12’s this year as potential upset candidates.  If Princeton can string together a run on three’s they have a shot, the Ivy league is well represented in our upset list.  UNC Wilmington shoots the ball extremely well, which they will need to upset Virginia, who is arguably the best defensive team around.  Similarly to Princeton, if Nevada gets hot they become a real upset threat.

The real value bets this year lie in the 13-4 and 14-3 rounds.  These teams get to avoid the Arizona’s, Duke’s, and Louisville’s who could legitimately vie for a one seed.  They are also as a whole really good shooters, probably the most important factor in determining the outcome of a college basketball game, especially an upset.  New Mexico State, Florida Gulf Coast, East Tennessee State, Bucknell and Vermont all shoot at above a 54% clip for their 2 point shots.  Iona and Winthrop are equally deadly from the 3.  The gems of this class are Bucknell, and my two favorites Vermont and East Tennessee State.  All shoot above 54% from 2 and 36% from 3, if they got hot look out.  They also play a little better defense than some of their peers with similar seeds.  Rounding out the 14 seeds, New Mexico State, FGCU, and Iona are all efficient shooters as well.  Kent State although they rebound well, they don’t shoot at an elite level and are likely going to be outmatched by UCLA the nations top offense.

What we didn’t see in this years class of potential upsets, are any teams excelling in creating turnovers or elite on defense.  However we were graced with excellent shooters, and some decent rebounding teams.  I would take field goal efficiency every day.  Anyways there you have it, my 2017 March Madness upset candidates.  Note I say candidates, many of these teams will go on to be blown out, but most will compete, and a couple are going to prevail, hopefully we have narrowed our upset contenders correctly.  There you have it, focus on the 13 and 14 seeds this year, particularly Vermont, East Tennessee State, and Bucknell if they can handle the pressure (see what I did there?)


Easiest Fantasy League I Ever Won

I am going to take a quick aside from college basketball to tell you about the easiest fantasy league I ever won, and how you could have too.  Most people are familiar with your standard formats of fantasy football, baseball, or basketball.  Those who follow hockey or soccer also have regular fantasy leagues.  However, one in particular, is less popular but provided perhaps the easiest opportunity to win I have ever participated in.  Enter, NFL Playoff Challenge.

The brief synopsis is you play for four weeks, following the NFL playoffs.  Each week you choose a lineup consisting of a QB, 2 WR’s, 2RB’s, a TE, K, and DEF.  There are no salary caps, no draft, you can choose a new lineup each week.  Scoring follows standard fantasy football formats (non PPR) with one exception.  The multiplier.  Each week you start the same player at the same position, you gain a multiplier to that persons score.  For example if you were to start Aaron Rodgers in the wildcard round, when they won he would have scored 2X points in the divisional round, and 3X points in the NFC Championship, and had they made it all the way, 4X points in the Superbowl.  One thing to note, is you can select players not playing in the wildcard round, and they will automatically advance to a 2X multiplier in the next round (although will not net any points in the wildcard round).  If a team is eliminated, you can choose a new player next week, however the multiplier will be reset.

NFL Fantasy Playoff Challenge Strategy

Lets look at some simple strategy.  Assuming we know nothing about the teams being played.  If we look at the odds of a team playing in the wildcard round to reach the Superbowl, assigning a 50% chance to win each game, they have to win 3 games to advance which gives them a 12.5% probability.  Note we don’t care whether they win the last game, just get to it, as there are no games beyond that so advancing is no longer a concern, other than the winning team will likely yield more points.  A team with an opening round bye, only needs to win 2 games to reach the Superbowl, giving them twice the odds of a team playing in the wildcard round.  So we now know that choosing a team that gets a bye, will give us twice the chance of reaching the 4X multiplier we want.  The goal is to maximize our points, so what would you rather have?

Wildcard player:
1X + 2X + 3X + 4X = Max points possible (10X) if reaches Superbowl but half the odds of doing so.

First round bye player:
2X + 3X + 4X = 90% of Max points possible, but twice the odds of wildcard player.

Under these assumptions, it seems obvious it is in our best interest to pick players given the first round bye.  Yet few people will do so, with names like Antonio Brown and Le’Veon Bell available in the wildcard round, its hard to pass up, even though they are not as likely as advancing to the fantasy league finals where the coveted 4X multiplier comes into play.

Thus far we have assumed all teams are created equal, which we know is not the case.  This year in particular all the favorites won the wildcard games, if we assume that was the case going in, we could justify taking the big names players on those teams.  The problem though, is picking which of the teams that advanced to the divisional rounds would make it to the Superbowl.  On the NFC side, we had Atlanta, Dallas, Seattle, and Green Bay.  Any one of which had a legitimate shot at winning.  How do we know which one we want to pick players from?  Do we guess, pick a sampling from various teams and hope for a more balanced approach?

No, absolutely not!  While the NFC was a crap-shoot, the AFC this year was a different story.   Enter the Houston Texans and Oakland Raiders.  One team with no business being in the playoffs with a QB throwing more picks than he did touchdowns.  The other team an offensive powerhouse who lost their starting and backup QB’s.  Who does the winner get to play?  None other than Tom Brady and the Patriots in the divisional round.  Chalk this up to a free win for New England, boosting their odds of reaching the Superbowl to at least 50%, assuming they are the favorites to win the AFC championship since they will host it at home.  See where I am going with this?  While the NFC is going to be a toss-up, I can fill my NFL Playoff Challenge team with Patriots, who have a better than 50% chance of advancing to the final game, and thus receiving the 4X multiplier.

There lies the secret of my fantasy playoff strategy this year.  Load up on Patriots.  Had New England lost to Pittsburgh, I would have no chance of winning.  However I weighed that risk, vs the riskier scenario of having to pick which NFC team would advance and the choice became simple.  I filled 6 out of the 8 positions with Patriots.  I could have chosen all 8, however it is hard to predict if a #2 WR or RB are worth it even with the multiplier given the lack of opportunities as their #1 counterparts.  Lucking out with Julio Jones as my other WR was an added bonus (originally had Jordy Nelson, but after he got hurt, I switched to Julio).  My other RB slot was reserved for E. Elliot, who didn’t advance, but having a player from the 1 and 2 seeded teams in the NFC gave me a good shot of getting one in the Superbowl for the 4x bonus.  While I was trailing the first 3 weeks of the playoffs, I can assure you my fantasy league opponents were horrified to see 6 out of 8 players with a 4X point multiplier next to their name, as well as Julio with a 3X.  Although they did talk some smack after a dud of a first half by New England, Tom Brady answered and turned it around.  Check cashed, easiest fantasy league I ever won!


March Madness Prop Bets

March Madness Prop Bets
After a thrilling Superbowl weekend, the thought crossed my mind, why don’t other sports offer more prop bets, particularly college basketball’s March Madness?  There are a lot of similarities.  Often times your team is not the one competing so another reason to root for something can be refreshing.  Sure, there is the argument that it is an amateur sport, and we shouldn’t be betting on kids, and the fear that some player may exploit it for profit.  However, how fun would it be to have readily available prop bets for March Madness?  You are throwing money down in your office pool, but once the opening weekend is done and you are all but eliminated wouldn’t it be fun to double down and have a little more action?  March Madness prop bets could be just the ticket.

Lets take a look at some possible examples:

What will be higher throughout March Madness?
+150 Grayson Allen Trips
-200 12 seed vs 5 seed upsets

Number of schools names mispronounced in the opening round?
-300 Over 2.5
+200 Under 1.5

Will Donald Trump fill out a bracket?
– 700 No
+500 Yes

Number of games decided by one point throughout March Madness?
– 110 Over 3.5
– 110 Under 3.5

Which conference will have more wins?
Pac 12  2/1
Big 10  3/2
WCC    2/1

Will the National Champion be a repeat winner?
– 200 Yes
+ 150 No

Will a 16 seed knock off a 1 seed?
Yes  20/1
No   1/20

What will be higher?
The number of games Gonzaga wins
The number of Kentucky players who declare for the draft

Other common types of March Madness prop bets are which seed will end up winning the tournament?  Or how many 1 seeds will reach the Final Four?  However, I particularly enjoy the offbeat comparisons style bets.  A would you rather of atypical scenarios, such as the Gonzaga example above.

So sure, you can find some prop bets in your favorite offshore casino.  However I would like to see a little more variety of prop bets come March in Las Vegas.  While to most, 68 teams competing for one title is probably more than enough action to bet on, some times you want to put a few bucks down on something stupid and have a little fun.  Whats the harm in that?   Any interesting prop bets you would like to see?

Betting College Basketball with Adjusted Efficiencies


Anyone who has visited Ken Pomeroy’s site or read Dean Oliver’s “Basketball on Paper” is familiar with the concept of efficiencies.  More simply put the amount of points scored per possession.  College basketball is a sport that can effectively be modeled using an expected offensive efficiency (points scored per possession) compared against an opponents expected defensive efficiency (points allowed per possession).  Combine that with the number of possessions per team, and you can predict the final score.

home score = home teams offensive efficiency vs visitors defensive efficiency * # of possessions
visitor score = visitors offensive efficiency vs home teams defensive efficiency * # of possessions.

A couple of clarifications are already needed to the above formulas.  We say “vs” between an offensive and defensive efficiency, there are different ways to calculate this.  If a team allows 0.8 points per possession and the opponents score 1.05 points per possession, what do we expect to happen when they play?  The first thought might be to average the two numbers.  This would be incorrect though, because a defensive efficiency of allowing 0.8 points per possession would likely be the best in the league, and assuming that is from playing a variety of opponents that likely have an accumulated offensive efficiency around the league average (~1.02 points per possession, varies year to year), playing an offense that scores slightly better we would not expect anywhere near the 1.05 efficiency they usually produce, we would probably expect somewhere between 0.8 and 0.85 points per possession.  We can bring league average into the equation (simplified for now, but assuming a team plays both good and bad opponents and over time will average out, we will get more accurate later on).  Say an expected offensive efficiency can be added to the opponents defensive efficiency, and then subtract the average efficiency out of it.  So in this case the expected defensive efficiency would be 0.8 + 1.05 (the opponents avg offensive efficiency) – the league average 1.02 which equals 0.83.  We would expect this opponent who scores slightly better than most teams in the league to similar score slightly more against team A.  Similar calculations can be made for offensive efficiency.

The other clarification is finding the exact number of possessions, which is not a stat provided in a typical box score, however can be estimated by counting the number of made shots (including trips to the free throw line), defensive rebounds, and turnovers.

Another flaw with what we have proposed is that teams play other teams of different strengths over the course of the season.  Often times some teams will have a much higher strength of schedule than others, which would tend to lead to lower efficiencies.  Fortunately we have ways of accommodating this.  We can look at the quality of opponents which a team faced and adjust our predicted efficiencies accordingly.  I won’t go into too much detail on how this is done here, the short of it is if we want to calculate team A’s adjusted offensive efficiency, we need to look at the defense of every team, team A has already played and compare it with the national average.  If they have played a weaker than average schedule, we would give them a bump in adjusted offensive efficiency, else we would reduce expectations.  See for a full description.

Betting on college basketball using adjusted efficiencies.

So now that we have an idea of what adjusted efficiencies are, I wanted to explore if these could be utilized to beat the spread and/or totals bet in college basketball.  We learned in an earlier post that most college basketball lines are set extremely close to these adjusted efficiencies.  However maybe there is a large enough discrepancy to exploit some weakness here.  So I designed an experiment to find out.

For this experiment I am using college basketball data collected from the 2003/2004 through the 2014/2015 seasons.  In order to calculate adjusted efficiencies I need a decent sampling of game data each season.  For that reason I am only considering games from January through the end of each season.  I don’t include any preseason rankings, or other prediction based approaches, I want this to be fueled by real data that resets each season, so I exclude the first two months from my simulated bets and use them only as data for calculating adjusted efficiencies.

I would have liked to compare with the adjusted efficiencies directly from  However, the data presented on that site is constantly changing as the season progresses.  There is no way to go back and view the adjusted efficiencies at a specific point in time.  I want a purely predictive model, so I needed a new approach.  To solve this problem I have decided to calculate my own adjusted efficiencies based in a way as similar to Ken Pomeroy as I can.  For this I calculated the raw offensive and defensive efficiencies, along with the predicted possessions for each game, calculated by:

(Field goal attempts – offensive rebounds) + turnovers + (0.475 * free throw attempts)

Then adjusted for competition as explained above.   So for each game, I looked at team A and every team B it had played prior in that season, and calculated team A’s average offensive efficiency, and adjusted it for each of team B’s defensive efficiency performances up until that point in the season against the national average.  So if team A averaged 1.05 points per possession (more than the league average), but their opponents adjusted defensive efficiency also allowed 1.05 points per possession (also more than the league average), I would adjust team A’s expected offensive efficiency to be the league average (1.02).  Similar calculations were made for the adjusted defensive efficiency.  Note that only games against Division I opponents were included in these calculations.

Before analyzing any results, I cross-checked my results with some of the late-season games each season, as these games should be the closest in comparison in my model to’s predictions.  They were not exact matches, as his model likely weighs other factors such as favoring recent games and possibly considering the site of each game played.  I have yet to find his exact formula for his calculations, however the values I cross-checked were reasonably close.  Each adjusted efficiency averaged to be within a 2% difference with his model, not exact but close enough for now.

Nerd Speak: For this experiment I wrote a C# program to create my model.  I load the raw data from csv’s and store into a sql database.  For each game, I query the database for the home and visiting teams, I load every Division I game played up until that point in the season, calculate the adjusted efficiencies looking not only at every game the home and visitors played, but also each game all of their opponents played in order to determine proper weights for my adjusted efficiency model.  For each game I output a predicted score for both teams and spit out into an Excel spreadsheet.  I use some simple functions in Excel to evaluate how the model did, and visually cross-check that my results seem realistic.

Adjusted Efficiency Betting Results

21584 games were used for my analysis.  While there were more applicable games in the January-April time frame for these college basketball seasons, I could only evaluate against games I could find betting lines for.  I had purchased a historical data set, which was mostly complete but had some holes.  My first approach included every game in this data set, evaluating against the closing spread and closing total line for each game.   Here were the results:

Spread bets:
Wins: 10617  Losses: 10523  Win %: 0.492

Total bets:
Wins: 10523  Losses: 11061  Win %: 0.488

Unfortunately, these results did not show any advantage.  My next step was to try to conclude why.  Perhaps because I am betting on every game, despite the differential between my prediction and the perceived advantage it might have over the spread.  To test this hypothesis, I decided to only consider games where my predicted score differed from the Vegas spread by 5 points or more, and 8 or more for the totals bet.  Lets look at the results.

Spread bets:
Wins: 1149  Losses: 1187  Win %: 49.18

Total bets:
Wins: 1337  Losses: 1450 Win %: 47.97

Again, not the results I was secretly hoping for.  There seems to be no advantage in using adjusted efficiencies the way I have to predict college basketball spreads or totals.  However, it did give me some evidence that my model was fairly accurate at predicting Vegas spreads as 89.2% of the games I predicted the score differential was within 5 points of the spread.  Considering my model does not count for injuries, other day to day lineup adjustments, or any perceived “hot streaks” that may influence the line one way or another I would say It is fairly a good prediction model, but one that is better at predicting Vegas spreads than beating them.

In this experiment we showed there is no easy button in beating college basketball spreads.  We can’t simply plugin kenpom efficiencies and hope to go break the bookies in Vegas.  However this won’t be the last we see of efficiencies, we can break them down into the four factors and look at how teams effective field goal percentage, offensive rebounding, turnovers, and free-throw rate match-up against their opponents, we will also tap into some machine learning approaches to try to dig deeper into understanding how to beat the college basketball spread.  More to come.