Notes from the Sports Nerds
SEC Tournament Bubble Game of the Night: Florida Vs. Mississippi State
- 0 comments - NCAA Basketball - posted by John Ezekowitz on 03.12.10
Tonight’s SEC Tournament quarterfinal between Florida and Mississippi State has stark and simple meaning. Win and your team could be dancing. Lose and it’s hello, NIT.
While Mississippi State may need another win to be in, with this year’s extremely weak bubble, a victory over Florida would do wonders for their candidacy. At the same time, Florida would probably be safely in with a win, and our power ratings based projections see Florida as the slight favorite.
These two teams are a study in contrasts. Mississippi State, led by the NCAA’s all-time leading shot blocker, Jarvais Varnado, plays extremely good interior defense, while Florida’s balanced offensive attack has allowed them to score an impressive 1.05 points per possession.
The Bulldogs are third in the country in block percentage and 8th in 2-point field goal defense. This adds up to Mississippi State being ranked 2nd in the country in our opponent shooting efficiency rankings.
Florida takes excellent care of the ball, turning it over only 15% of the time. They also crash the offensive glass, rebounding almost 39% of their misses. However, Florida’s greatest offensive strength may well be its balance. No less than six Gators play major usage roles in the offense, and in most offensive sets, everyone on the floor is a scoring threat.
Mississippi State, on the other hand, has struggled to find offensive efficiency when they are not shooting the lights out. For a tall team, the Bulldogs do not hit the offensive boards well, rebounding only 30% of their misses, and they do not get easy points in transition as they do not force many turnovers.
It seems that only when the Bulldogs manage to shoot above 50% from the field that they are very likely to win. Unfortunately, as the table above shows, in SEC Conference play MSU has had more trouble reaching this benchmark.
In their regular season loss to Florida, the Bulldogs only shot 45% from the field. Mississippi State’s impressive defense may slow down Florida’s balanced attack, but their offensive will need to respond with a better-than-average night to help keep their NCAA Tournament dreams alive.
Big Ten Tournament Preview: Can Minnesota Upset Michigan State?
- 0 comments - NCAA Basketball - posted by Neil Curran on 03.12.10
For the teams like Big Ten Tournament #6-seed Minnesota that want to keep playing past this weekend, they have to play well this weekend. At 19-12, Minnesota is right on the bubble and a win against 3-seed Michigan State would certainly resonate among the NCAA Selection Committee come Sunday.
Our Big Ten Conference Tournament Projections think an upset here is a pretty good possibility.
How feasible is a Minnesota win tonight? In their previous meeting, Minnesota led the entirety of the game until the last 1:30. Out of steam from an overtime loss a few nights before, the Gophers went on to lose by one. Keep in mind that was also the first game without starting point guard, Al Nolen, who has been ruled academically ineligible for the remainder of the year.
So expect tonight’s contest to be hotly contested, primarily because with the exception of win-loss records, these teams look extremely similar from a statistical point of view. (See full matchup analysis)
On offense, they are both the elite of the Big Ten in scoring and efficiency. Minnesota leads the conference at 73.5 points/game with Michigan State not far behind at 72.8 points/game; offensive efficiency is separated by one one-hundredth of a point (1.077 versus 1.067 respectively).
The only significant discrepancy on the offensive side is offensive rebounding percentages; Michigan St holds the advantage at 39.7% compared to 32.0%. I’d watch this stat throughout the night as total rebounds were close last game (29-32) with the exception of 4 more offensive rebounds for Michigan State (6-10).
Defensively, the story is no different. While both teams fall into the top half of the Big Ten, they are not extremely impressive. Both teams hold their opponents to effective field goal percentages of 46%. The Gophers have been giving up .935 points per possession this season compared to the Spartans .929 points per possession. Statistically, these defenses are nearly mirror images with no one holding any real statistical advantage.
This should definitely be a game worth watching, as Minnesota’s season is on the line. Don’t be surprised if it’s a nail biter, and rebounding performance could likely be the difference maker.
Bubble Game of the Night: Georgia Tech vs. North Carolina
- 0 comments - NCAA Basketball - posted by John Ezekowitz on 03.11.10
Georgia Tech and North Carolina were not supposed to meet on Thursday at the ACC Tournament. In the preseason, most experts would have thought that this game would be for seeding or possibly for the ACC Tournament title. These teams have been two of the biggest disappointments in college basketball this year; their obvious physical talents have not produced enough wins.
The postseason offers a fresh start. Georgia Tech must beat UNC to keep their at-large hopes alive, while the Tarheels are hoping to start an extremely unlikely run (how unlikely? according to our 2010 ACC tournament projections, UNC should win the title only 1 in every 130 tries) to the ACC crown. Georgia Tech comes in a slight favorite, but there is really not as much separating these teams as their records would suggest.
The Yellow Jackets and Tarheels are surprisingly similar offensively. Both score 1.01 points per possession, get about 38.5% of their offensive rebound opportunities, and struggle with turnovers. The real difference is on the defensive end. Georgia Tech plays very good field goal defense, allowing their opponents to make only 42% of their attempts from the field. North Carolina is more permissive, allowing opponents to make 47% of their attempts. (See full UNC vs. Georgia Tech matchup analysis)
This is a key stat for this game, as this season UNC seems to play significantly better defense when they shoot the ball better. Thus if Georgia Tech can limit UNC on the defensive end, it could have positive consequences for their offensive efficiency. Below is a scatter plot of North Carolina’s defensive efficiency and field goal percentage. This year, there is a correlation between when UNC shooting the ball well and playing better defense.
If Georgia Tech can force North Carolina into a bad shooting night, just as they did in their regular season win over UNC, then they stand a much better chance of winning the game, as there may be defensive implications for UNC as well.
The key for North Carolina will likely be to force Georgia Tech into turnovers and convert those turnovers into fast break points. The Jackets have not had a true point guard all year, and their three starting guards, Mike Bell, Iman Shumpert, and Mfon Udofia, all have assist to turnover ratios under 1. If North Carolina can keep the pace high through turnovers and easy baskets, they could pull off the minor upset.
NCAA Basketball ATS and Over Under Picks Launched
- 0 comments - NCAA Basketball - posted by tom on 01.08.10
We just wanted to put the word out that tonight we launched our NCAA basketball ATS predictions and college basketball over under predictions for the 2009-10 season.
We have developed a new, advanced statistical model we’ve named the Decision Tree model, which is the source of our initial NCAA basketball betting picks.
It’s off to a solid start this year; 2-star or better NCAA basketball ATS picks are 260-213-12 (55%, +23.4 units) as of this posting. As usual, we’ll offer these NCAA betting picks free for the next week or so to give our users a test drive.
NCAA Basketball ATS Picks
NCAA Basketball Over Under Picks
New NCAA Decision Tree Model Page
Updates to Win, O/U, ATS Predictor Tools
- 0 comments - College Football, NFL - posted by tom on 01.07.10
Today we released a fairly significant change to our Win Predictor, ATS Predictor, and Over Under Predictor tools.
In their first incarnation, these tools were built to enable users to do three things:
1) Choose a specific set of games to predict, since some people only care about certain conferences or teams and because pick’em contests vary regarding what games are included each week
2) Customize betting lines for each game, again useful for spread-based pick’em games that freeze game point spreads early in the week, or for matching betting odds to those available to you at your sports book of choice
3) Create a data-driven predictive strategy based on stats of your choice to apply to all games, and then view the resulting picks
In short, we tried to do a little too much in one product, and today we moved to simplify the user experience.
First, we have completely removed item #3 from our Win, ATS, and Over Under Predictor tools. Now, you choose games to predict, pick a pre-existing predictive model to use, and click to generate picks. Much faster, much simpler, much easier.
Best of all, you no longer need to create your own customized prediction model to get picks. You can just use one of the predictive models already created by the Team Rankings nerds. But unlike getting predictions from our Picks page, in our Predictor tools you can alter the applicable betting lines of any game.
(Our picks pages just show predictions based on the most recent betting odds we get from our primary odds supplier.)
Second, we have built a separate tool, Model Builder, for more advanced users who are interested in creating and backtesting their own data-driven predictive strategies. This tool allows you to build a predictive model based on stats of your choosing, then backtest its performance over recent games, and save it to your Team Rankings user account.
Now, as soon as you save a custom model using Model Builder, bam — your newly saved model appears as one of your prediction model options in the Win, ATS, and Over Under Predictor tools.
We’ve still got a little cleanup work to do on the user interfaces, but the new versions of the predictor tools are released and functional. Take one for a spin:
NFL Win Predictor
NFL ATS Predictor
NFL Over Under Predictor
NFL Model Builder (for advanced users)
Side note to current tool users: If you used the prior versions of Win, ATS, or Over Under Predictor to save custom predictive strategies, the updated Predictor tools should automatically load your custom models into the new interface.
The Impact of Resting Starters On NFL Predictions
- 3 comments - NFL - posted by tom on 12.19.09
Now that we’re nearing the close of the 2009-10 NFL regular season, it’s a good time to investigate the impact of resting starters on our site’s algorithmic NFL picks and prediction models.
(Note: When we say “resting starters”, we use that term to refer to resting starters entirely, pulling starters early, or any other end-of-season inspired player usage strategy.)
If you don’t care to read all about this, here’s the quick summary. Based on historical performance over the last four years during the late season:
- Expect reduced performance from our models in terms of picking game winners and vs. money lines
- Be wary of applying Power Ratings model predictions to any game in which resting starters is expected
- The Similar Games (Odds) model has performed best ATS during weeks 16 & 17 (counting all games)
Here’s the long version:
Team power ratings either fully or partially drive our NFL prediction models. At its most basic level, a power ratings based game prediction assumes that a team will perform at the same level at which it has performed in the past.
There are variations on this theme, since some of our models look more at recent performance, others at full season performance, etc. But the bottom line is that if a coach suddenly decides to use players and/or strategies in a much different way than he has done in the past, the underlying data on which any power rating is based becomes less relevant.
When it comes to our NFL prediction models, we have one model (Power Ratings) that incorporates only power ratings when making game predictions, and another model (Similar Games) that incorporates power ratings, betting odds, and other stats and data.
To get a sense of if and how resting starters may affect prediction performance, we decided to do a quick exploration of model performance during NFL Weeks 16 and 17 over the last four years.
This isn’t a great proxy, because it’s a small sample of games and it’s also likely the case that, due to the randomness of team scheduling, some years feature more “resting starters games” than other years. But we were interested in the results nonetheless. Here’s what we found in terms of Week 16 and 17 results:
- Both models have done worse picking winners (58% and 57%) than they usually do (63-69%). It’s logical to conclude that games in which late season personnel changes were in play were the primary cause of that performance decline, but we can’t be 100% certain of that based on this analysis.
- ATS prediction performance dropped somewhat for the models primarily based on power ratings or expected scoring; the Power Ratings model was 52% ATS overall in Weeks 16-17 (although it did go 23-9 ATS during the last two weeks of 2006, it also had two horrible years) and the Similar Games (Points) method was 50% ATS.
- ATS prediction performance for the Similar Games (Odds) approach has been good (57.6% ATS), with fairly consistent performance across the last four years. This higher level of performance does make logical sense, as this model is largely driven by point spreads, which (at least to some degree) reflect the expected effects of resting starters
Given that game winner prediction performance has historically decreased during the last two weeks, it’s likely the case that money line prediction performance will decrease as well, as a result of resting starters games.
We want to share this information to make sure our users are aware of it. The fact is that predicting the impact of resting starters with a high degree of precision is extremely difficult. In most cases, it’s a crap shoot when a coach may pull his starters, and it often depends on a variety of intangible factors or the timing of team scoring throughout a game.
As a result, given the data available to us, we don’t play any guessing games by trying to alter our models in any way to “adjust” for clinched (or bad) teams emptying their benches near the end of the season.
If you believe resting starters will likely be a factor in a game, it’s probably best to focus on the Similar Games (Odds) model for that game, or use our NFL Game Predictor tool to build your own algorithm that emphasizes the Point Spread and Game Week factors much more than power ratings.
For games in which both teams should play to the best of their abilities to the bitter end, of course, none of the above precautions applies, for Week 16 at least. However, a future post will discuss the potential impact of a good team losing a “meaningless” game on league power ratings.
| Win | Won | Loss | Push | % |
| 2005 | 20 | 12 | 0 | 62.50% |
| 2006 | 13 | 19 | 0 | 40.63% |
| 2007 | 21 | 11 | 0 | 65.63% |
| 2008 | 19 | 13 | 0 | 59.38% |
| Total | 73 | 55 | 0 | 57.03% |
College Bowl Team Rankings and Bowl Team Stats
- 0 comments - 2009-2010 College Football Bowls - posted by tom on 12.14.09
Today we’re happy to announce the release of our new college bowl team rankings and college bowl team stats pages:
2009-10 College Bowl Team Rankings
2009-10 College Bowl Team Stats
To make it easier to sort and compare the relative performance of the 2009-10 college football bowl teams, we’ve created independent stats and power ratings pages only list teams in college bowl games. This way, you can quickly become a college bowl data guru, and make smarter bowl picks.
For example, did you know that…
- Marshall is currently our worst rated bowl team in away game performance?
- Alabama has the highest strength of schedule power rating of any bowl team?
- Boise State leads all bowl teams in turnover margin per game, at +1.7?
- TCU has done best at limiting opponent third down conversions (26.5%)?
So drop whatever clearly less important thing you’re currently doing and start memorizing some bowl team numbers!
College Bowl Prediction Tools Launched
- 0 comments - 2009-2010 College Football Bowls - posted by tom on 12.11.09
It’s now Day 4 of our 12 days of Bowlsmas, which means we’re rolling out more college football bowl related features.
Today’s treats are our user-customizable bowl prediction tools: Win Predictor, ATS Predictor, Over Under Predictor, and Game Predictor. These tools allow you to create your own statistically-driven prediction strategy, and with the click of a button, apply your prediction strategy to all of the 2009-10 college bowl games.
We’ve found that everyone has a theory for what it takes to win college football games. Some say defense wins championships, other say that passing efficiency and stopping the run are all the matter. So we built our Predictor series of tools to let users see what a data-driven prediction model based on their theories would forecast.
Whether you’re in a college bowl pick’em contest, planning on betting some of the bowl games, or just curious to see how different prediction approaches would forecast each game, our college bowl prediction tools offer a quick and easy way to do unbiased game research.
College Bowl Winner Predictor
College Bowl Game Predictor
College Bowls ATS Predictor
College Bowls Over Under Predictor
New Feature: Backtest Your Own Prediction Strategies!
- 0 comments - College Football, NFL - posted by tom on 12.10.09
We’re extremely excited to announce the release of the first version of the Team Rankings Model Builder, a technology and application that we have been working on for quite some time.
Over the past weeks, months, even years, we’ve heard from people who use our sports prediction tools to create unbiased, data-driven algorithms for predicting games. Many of our users have enjoyed strong success with sports betting and in pick’em contests using their own customized Team Rankings prediction models.
However, the most common — and understandable — request we get is this: “If you guys could devise a way for me to not only create my own objective algorithms for predicting games, but also be able to quickly test how my strategies have done in the past, that would be huge.”
We’re happy to say that today, we’ve brought that idea to life. Instead of creating a predictive strategy and having to wait weeks to measure how it performs, now you can see how your theories have played out over the last 100 games — in seconds.
Not only that, but Model Builder shows you how your custom prediction strategy has done picking game winners, ATS (against the spread) winners, over under line winners, and money line winners. And it displays both percentage correct and betting unit based performance results.
We already have a number of enhancements on the list for Model Builder, but the core is in place. Check it out and let us know what you think. It’s powerful functionality, but we also hope we’ve made it easy and fun.
So stop following the herd and use Team Rankings to craft your own proven winning strategy!
2009-2010 College Football Bowl Picks Released
- 0 comments - 2009-2010 College Football Bowls - posted by tom on 12.10.09
It’s day 2 of our 12 days of Bowlsmas, and today’s bowl-related release includes our algorithmic bowl predictions and math model picks for all of the 2009-2010 college football bowls.
These bowl picks are generated from our 100% objective mathematical prediction models and include college bowl winner picks, college bowl ATS picks, college bowl over under picks, and college bowl money line picks.
College Bowl Picks
College Bowl Winner Picks
College Bowl ATS Picks
College Bowl Over Under Picks
College Bowl Money Line Picks