Home > Other > 2012 NCAA Bracket Methodology: The RUN System

2012 NCAA Bracket Methodology: The RUN System

Well, it’s that time of year again. March Madness begins in earnest tomorrow, and I’ll be looking to defend my championship from last year’s FOTB Bloggers Cup. My method last year revolved around ranking teams by a combination of SRS and Win Shares. For a quick refresher on those two terms, feel free to check out last year’s article here.

This year I’ve decided to expand my system by including a few new statistical categories that I think are important to tournament success. This was done mostly because my long-term plans include developing arthritis, and I figured that wasting a couple hours over at www.sports-reference.com/cbb (the holy grail of basketball statistics sites for nerds like myself) would help expedite the process.

The result is called the RUN system (Ridiculously Unnecessary Numbers).

First, here’s my bracket:

In the interest of time, I’ve used my methodology to come up with a team score for the top 32 seeds. Historically, no team has won below an 8 seed, and I don’t foresee that changing this year.  However, upsets always occur every year, so I will make a couple predictions based on gut feeling in the first round, but the team scores I calculated will ultimately decide the later rounds of my bracket.

The team scores are calculated using the following breakdowns. The team that scored the highest in the category receives the full percentage allocation (ex. 10% for free throw percentage), and the other teams receive a weighted fraction. So, if the top team shoots 85% from the line, and the next highest team is at 80%, team 1 would receive the full 10% and team 2 would receive (80%/85%) * 10%. Got it? Let’s go:

SRS (25%) – Self-explanatory from last year’s article I referenced above. A higher SRS indicates a team played a more competitive schedule, and had a higher average margin of victory. This number can reward teams that played difficult schedules, as well as teams who have the offensive firepower to pull away from teams without suffering prolonged slumps.

Win Shares (10%) – As stated last year, this statistic predicts how many wins a player is directly responsible for over the course of a year.  For example, a win share of 5.0 for me would mean that was estimated to have contributed 5 wins to the team, versus if they had a complete pylon out on the floor. I included this metric because of the nature of the tournament, where a hot player can take over games. I make the assumption that a higher win share means that the player is generally comfortable being the go-to guy on the team, and has pulled out victories in the past. If you’re really a huge dork, here’s some light reading on how this statistic is calculated.

Guard Block Percentage – Top 3 Average (10%) – Alright, we’re heading into obscure country now. I’ve noticed in the past that many tournament games come down to final shots in high-pressure situations. This metric captures an estimate of the number of two-point FG attempts blocked by the top 3 guards on the team, based on total minutes played during the season. A higher number here means that the guards are likely bigger, faster, longer, or all of the above, and will disrupt far more shots in the post in close game situations, where the defense collapses into the key. Ideally, this metric will reward teams with guards who can disrupt shots, which is a critical factor in securing wins in close games during the last ten or fifteen seconds.

Offensive Rebound Percentage – Top 3 Average (10%) – Like the previous metric, but applied to the top three players on the team on a total minutes played basis. For teams that play their big men consistently more than their guards, this number should be higher. As a result, it should indirectly capture the chemistry between the big men if this is the case. Alternatively, for teams that play their guards a high number of minutes, it penalizes them slightly because of the fewer offensive rebounds you would expect the team to get over the course of the tournament.

Turnover Percentage – Top 5 Average (25%) – I weighted this factor quite heavily, because it is fairly common for NCAA tournament games to come down to the last possession. The difference between having a 13% turnover rate and a 17% turnover rate can thus be the difference between winning and losing. For this metric, I calculated the average turnover percentage rate for the players with the top 5 highest minutes played during the season. This is my best attempt to replicate the average turnover rate you would expect with the five best players on the floor for any given team.

Free-throw Percentage – Top 5 Average (10%) – This is weighted slightly less than might be expected, because of the success of some historical teams (2008 Memphis comes to mind) in the tournament despite terrible free throw shooting. The 10% weighted to it will reward those teams that have consistently strong shooters through their line-up, even though at the end of games most teams have one strong player that they can delegate free-throw shooting to if needed.

So, here are the team scores once the metrics have been applied:

Seed

School

TEAM SCORE

1

Kentucky

82.5%

1

Michigan State

75.4%

1

North Carolina

81.3%

1

Syracuse

74.3%

2

Duke

69.0%

2

Kansas

70.7%

2

Missouri

69.4%

2

Ohio State

78.5%

3

Baylor

78.6%

3

Florida State

70.1%

3

Georgetown

69.9%

3

Marquette

67.7%

4

Indiana

74.3%

4

Louisville

67.7%

4

Michigan

64.0%

4

Wisconsin

74.4%

5

New Mexico

72.3%

5

Temple

60.3%

5

Vanderbilt

69.2%

5

Wichita State

69.7%

6

Cincinnati

63.0%

6

Murray State

59.9%

6

San Diego State

54.8%

6

UNLV

68.8%

7

Florida

65.9%

7

Gonzaga

59.4%

7

Notre Dame

59.5%

7

Saint Mary’s (CA)

64.1%

8

Creighton

61.5%

8

Iowa State

53.3%

8

Kansas State

67.1%

8

Memphis

69.4%

It’s interesting to see that my methodology does capture the differences in strengths between the different seed levels. Seeds 5-8 are often interchangeable, and the percentages show that, while seeds 1-4 are clearly stronger than the rest of the pack. Keep this in mind before predicting a 12-13 seed regional final.  When predicting first and second-round matchups, I tended to call upsets on the teams near the bottom of my power rankings (San Diego State and Iowa State were the bottom two).

The best part of all this is that Kentucky showed up first in my team scores, and I don’t have to write another paragraph defending my blatant Wildcats homerism. Last year I had them (correctly) in the Final Four; this year they’ll be taking the whole championship, over other Final Four teams Michigan State, North Carolina, and Ohio State.  On the whole, this bracket is the most conservative I’ve ever predicted (outside of the first-round upsets that I’m hoping for), but I’m sticking to the numbers this year in an attempt to win a second FOTB championship.

The top four teams I ranked are Kentucky, North Carolina, Ohio State, and Baylor, but Baylor suffers as a result of meeting Kentucky in the South regional final. Baylor is only 3.9% Team Points behind Kentucky in my rankings though, so if they slip through to the Final Four they could do some serious damage.

Final Prediction: Kentucky over UNC in the final, 83-72.

Last Thoughts:

Biggest team I would gamble on: Baylor (highest offensive rebounding in my metrics, lowest turnovers, and very close to the top free throw percentage). Also, Florida State because of their big back-court and resulting high guard block percentages.

Biggest team I would avoid: Syracuse (low win share total after Melo has been ruled ineligible for the tournament, poor free throw shooting, mediocre other metrics aside from SRS)

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