THE POWER RANK

How to win your 2020 March Madness pool

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In last year’s Final Four, Virginia was down 2 with 1.5 seconds left to play against Auburn. Ty Jerome inbounds the ball to Kyle Guy, their best shooter, deep in the corner. Guy launches a 3 point shot that could win the game.

There’s a reason I told members of The Power Rank to pick Virginia in their March Madness brackets, and not the favorite Duke. This advice looked great in the Elite 8, as Duke lost to Michigan State and Virginia advanced over Purdue.

This advice continued to look great late in the Final Four game, as Virginia had a 10 point over Auburn with 5 minutes left in the game. Then Auburn went on a 11-0 run to take the lead.

Virginia still had a chance to win, but Guy’s three pointer bounced off the rim. It looked like the end Virginia’s tournament hopes.

However, Guy got fouled on the 3 point shot. He went to the line to sink 3 free throws for a 63-62 Virginia win.

Virginia went on to beat Texas Tech in the national title game, and many of my members won their pool.

There are two lessons from this experience. First, it takes a lot of luck for your team to win the NCAA tournament. While Guy did get fouled, you can imagine that the referee doesn’t make that call.

Second, you need the right strategy to win your pool, and this depends on pool size. Analytics plays a role, as the strategy is based on the win probabilities for each team. However, in 2019, the right strategy for certain pools said not to pick Duke, the team with the highest win probability.

I explain this balance of analytics and strategy in my book How to Win Your NCAA Tournament Pool. Over the years, it has helped many people win their March Madness pool.

This is what L.S. Stint said after last year’s tournament.

I used Ed’s methods and won a pool with 102 entrants. As a woman, it was particularly gratifying to win as pool consisted of mostly men who consider themselves knowledgable. Thank you, The Power Rank!

— L.S. Stint, Ann Arbor, MI

A new version of the paperback is now available. This version has a new design and crisp images not usually found in self-published books on Amazon.

My book How to Win Your NCAA Tournament Pool is also available to members of The Power Rank. Members also get access to my complete bracket advice in which I apply the ideas in the book to the 2020 tournament. Members also get access to my daily college basketball predictions.

To learn more, click here.

NCAA is meeting with quants to make tournament selection process better

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On Friday, January 20th, 2017, hell will freeze over, and the NCAA will meet with analytics guys like Ben Alamar, Jeff Sagarin and Ken Pomeroy. The conversation will revolve around making the tournament selection process better.

You can read about it here, but two points stand out for me.

First, they say the following about the RPI rankings the committee currently uses.

An even more powerful microscope to go with the time-honored RPI.

Time-honored my ass. The RPI is stupid for two reasons:

  • It lacks a solid mathematical basis (compare it with the least squares rankings that Pomeroy uses)
  • It uses wins and losses instead of margin of victory in its calculations

I discuss both of these issues in relation to college football here. Hence, RPI fails as a predictor for how teams fare in the tournament.

The NCAA should eliminate RPI from the selection process.

Second, Jim Schaus, the athletic director at Ohio State and committee member, said this:

I’m going to have to strap on in the meetings to stay up with all the calculus that’s going to be discussed, but I’m excited about it.

Calculus is so overrated in our society.

You want to hang with the quants, Schaus? Then let’s talk probability, or that no analytics ever says a team will beat another team with 100% certainty.

Want to get fancy, Schaus? Then let’s dig into linear algebra so you can understand the least squares method used in adjusting for strength of schedule.

I’m all for learning calculus. It’s just not as useful in sports analytics as probability and linear algebra.