Our Similar Games Model uses data driven algorithms to identify historical NCAA tournament games that featured statistically alike teams competing under similar matchup circumstances.

For example, imagine that a first round matchup features a high scoring team from a weak conference playing a low scoring team that turns the ball over a lot. Both teams are traveling moderate distances to a neutral site arena. Similar matchup scenarios most likely have occurred in recent history, and the Similar Games Model identifies them and analyzes their outcomes.

Final predictions depend on the aggregate analysis of a number of data points about each identified similar historical game, such as which team won, by how much, how many points were scored, what results were implied by betting lines, and the relative degree of similarity to the current game.

Strengths: This model incorporates a range of power ratings and team stats as well as several contextual factors including Vegas line implications, travel distances, and game timing.

Weaknesses: This model does not explicitly consider several difficult-to-model factors such as recent injuries or days rest. If you feel one of those factors may have a material impact on the outcome of a given game, it may be wise to apply subjective adjustment to its predictions. Also, in certain cases, highly uncommon matchup scenarios make it impossible to find many relevant historical matchups.

The theory behind our Simulation Model is to use possession based statistics (also known as 'tempo-free' or efficiency statistics) to project the likely outcome of a game.

Possession based stats are better measures of team performance than "per game" stats primarily because the pace of a basketball game is an important driver of the final outcome. For example, imagine two teams are playing each other. One team turns the ball over an average of 15 times a game, while the other only turns it over seven times a game. Which team takes better care of the ball?

Of course, that's a trick question. What you really need to compare is how efficient each team is at handling the ball. If the first team typically has twice as many possessions per game as the second team, then in reality, these teams probably have about equal ball handling skills.

In our Simulation Model, we first generate what essentially are power ratings for a team's performance in major stat categories (blocks, rebounds, etc.). We then look at the number of possessions each of two opposing teams typically has, compared to benchmarks, and estimate the number of possessions we expect in the game being modeled. Given that number, we can use the individual stat ratings for each team to project what will happen on every possession, leading to a score prediction and expected box score.

Strengths: Unlike the more abstract power ratings and similar games models, the Simulation Model takes a very "bottoms up" approach to analyzing the playing styles of two teams, how they match up, and how specific statistical differences (e.g. a strong rebounding team playing against a weak rebounding team) are likely to effect the final score. The data used is all from this season, and each statistical rating for each team is adjusted for opponent strength.

Weaknesses: There more assumptions at play in this model than in other models. Primarily, we are assuming that a team will continue to play its same general style of basketball as its season stats so far imply. That's usually a safe assumption, but if a coach makes a radical change in game strategy or play calling for a given opponent, the raw data on which this model is based loses relevance.

Our Predictive Power Ratings Model iteratively analyzes data on every intra-Division I NCAA basketball game result this season. In the end, every team receives a simple numerical rating (e.g. 52.4 or 84.7) indicative of its actual, documented, proven track record at outscoring opponents.

Like fine wine, predictive power ratings tend to improve with age. The more game results data recorded for a given season, the lower the potential impact of luck on a team's overall performance results, and the more "connections" the model can make between teams in conferences of varying strength.

By comparing the predictive ratings of any two teams, you can determine projections for the game winner and expected margin of victory if those teams played one another. Calculating win odds is more complex; a formula translates the differences in predictive ratings into respective win odds for each team.

If you went back and recreated the 2008-09 NCAA basketball season using the final predictive power ratings for every team and the prediction methods described above, all 300+ Division I teams would end up with margin of victory performance and a win-loss record equal or very close to what actually happened.

Strengths: Predictive power ratings are relatively abstract measures that cut through media hype and rate teams based on actual scoring differentials, adjusted for game location and opponent strength. You can blab all you want about a team's legendary coach, elegant offense and twin 7-footers -- but who the heck cares if they still don't outscore the average opponent better than 40% of the other teams in Division I?

Weaknesses: The Predictive Power Ratings Model is a dynamic and reactive system. If an absolutely key player gets injured or a bad team all of a sudden gets fired up and starts playing well, it could take several games for the impact of those developments to make a big difference in predictive ratings. Abstraction has a downside too; if a huge situational mismatch exists between two particular teams (e.g. one team has an eight foot tall center and its opponent has no player over 5'5"), predictive ratings have no idea.

Adjusted scoring margin (ASM) calculations measure teams based on whether they score more or fewer points than their opponents on average give up, and vice versa. It's a somewhat similar theory to predictive power ratings but implemented in a different way. Every team has both an offensive and defensive ASM, and the sum of these two numbers is a team's overall ASM.

For example, imagine that at this point in the season, each of Team A's opponents has allowed an average of 70 points per game. However, Team A has scored an average of 75 points per game against that set of opponents. Team A's offensive ASM is therefore +5, which is a good thing. On average, Team A has scored five points more than its opponents typically give up.

Likewise, if Team A's opponents, on average, score 80 points a game, but manage to score 90 points a game against Team A, then Team A's defensive ASM is -10; that's not so good. (For consistency, we express good ASM's as positive numbers, and bad ones as negative numbers.) Team A's overall ASM is therefore an unimpressive -5, the sum of +5 and -10.

When two teams play each other, we can compare their respective ASM's to project the game's expected winner, win margin and final score, although you can get to those numbers a few different ways. ASM's accuracy tends to improve as more games are played.

Strengths: Looking at statistics in isolation often can be misleading, but adjusted scoring margins are relative measures. Giving up an average of 90 points a game may look bad compared to a league average, but not if you find out that the specific opponents a team has played actually average 105 points per game.

Weaknesses: The ASM method typically does not apply well when comparing teams with large differences in schedule or conference strength. Holding an ACC team to two points above its season average is probably a better performance than holding a Colonial League team to 2 points below its season average.