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Data is king. When we’re making any decision of consequence about the future — whether in business, sports or politics — our tech-filled world demands that we collect data to make a meaningful prediction.Despite the criticism that gets leveled at the NCAA tournament selection committee, the group does take a data-driven approach to choosing the competitors for March Madness. The RPI, or Ratings Percentage Index, helps the committee select and seed the field of 68 teams. The RPI takes into account all college basketball games and measures not only the strength of each team’s opponents but strength of its opponents’ opponents as well.In contrast, the preseason AP and Coaches polls don’t use data from the current season. They can’t, as members must submit their ballots before seeing a single game. It’s the ultimate in gut-level, subjective analysis.So surely the RPI rankings from the end of the season are a better predictor of tournament success than the preseason polls, right? Right?Nope. The RPI doesn’t beat out the early polls — well, at least not by one measure. The margin between them is admittedly narrow — they all predict the correct result around 70 percent of the time — but the fact that it’s so close shows how remarkable the gut-instinct ratings are and how flawed the RPI is.To find this result, I looked at how often the team that was ranked higher by each system won a tournament game. This study includes the past 16 years of men’s NCAA tournaments, or 1045 games, including play-in rounds.The RPI ranks every Division 1 team, so it makes a prediction in every tournament game.1For this study, I used the RPI rankings as they stood after all the regular-season games and the conference tournaments had been played. The polls, by contrast, rank only the top 25 teams, leaving most of the eventual March Madness field unranked. To partially correct for this, I included all teams that got votes in the polls and ranked them according to the same points system that determines the top 25. For game outcomes, any ranked team is predicted to beat any unranked team, a higher-ranked team is predicted to beat a lower-ranked one, and whenever two unranked teams meet, no prediction is made.2In a handful cases, teams that had the same number of points in the poll faced off. When that happened, no prediction was made.Over the past 16 tournaments, the RPI’s higher-ranked team won 69.2 percent of tournament games (723 correct, 322 incorrect). Surprisingly, the preseason AP poll did slightly better, as the higher-ranked team won 71.8 percent of tournament games (674-265, with no prediction in 106 games).The preseason Coaches poll picks games with the same accuracy of 71.8 percent (671-263 with no prediction in 111 games).3Given this sample of games, the error in the prediction rate is 1.5 percent. The preseason polls are a decent March Madness barometerBased on preseason polls and pre-tournament RPI rankings, how often higher-ranked teams beat lower-ranked teams in NCAA men’s basketball tournament games from 2002-17 To see the predictive power of the preseason poll, consider Wisconsin in last year’s tournament. The Badgers ranked ninth in the preseason AP poll, as they brought back Bronson Koenig and Nigel Hayes, two starters on the 2015 team that lost to Duke in the title game.Wisconsin didn’t live up to preseason expectations during the 2017 regular season. The team was 25-9 heading into the tournament, ranking 35th on the RPI, which resulted in a No. 8 seed. However, they beat No. 1-seeded Villanova in the Round of 32, and then came within 1 point of beating Florida to make the Elite Eight.What can we learn from this study? Is data no longer king when it comes to college basketball?For one thing, the preseason polls harness the wisdom of crowds, a surprisingly powerful predictor. No one ballot is perfect, as each will make some bad calls and reflect a person’s biases. However, putting many ballots together helps cancel out these small errors and leaves a powerful predictor of team strength. FiveThirtyEight has known this for years and incorporated these polls in its NCAA tournament predictions.In addition, the RPI is a poor predictor because it restricts itself to wins and losses. More accurate methods use teams’ margin of victory or points per possession to make rankings and predictions. These approaches do a better job of stripping away the noise built into wins and losses, as a team’s record can look very different depending on the outcomes of a few fluke buzzer-beaters or blown calls.This year’s tournament might be a particularly good one to demonstrate the predictive power of the preseason polls. Before the season started, Duke was ranked first and Michigan State was ranked second in both the AP and Coaches polls. However, these teams underachieved to end up as the 2 and 3 seed, respectively, in the Midwest region (and 6th and 15th in RPI).The preseason poll should remind us why these two teams are capable of deep tournament run: They have superior talent. Duke has three first-round picks, according to the latest ESPN NBA mock draft — big men Marvin Bagley III and Wendell Carter Jr., and shooting guard Grayson Allen — with point guard Trenton Duval listed as a second-round pick. Michigan State features two projected lottery picks — Jaren Jackson Jr. and Miles Bridges — along their front line. Both teams have the next-level talent needed to win six straight games and cut down the nets on April 2.What team might be overrated according to the preseason poll? Virginia, the No. 1 seed in the South region, tops this list, as they didn’t crack the top 25 in either preseason poll. Virginia always plays incredible defense, but this team lacks for NBA talent.Data is king, but only if used properly. The RPI is a poor method for evaluating college basketball teams. To get an edge in your tournament pool, you might be better off ignoring the metric the committee uses and looking to the preseason polls, a surprisingly powerful predictor of the tournament.Check out our latest March Madness predictions.Ed Feng has a Ph.D. from Stanford and developed the predictive algorithms for his sports analytics site The Power Rank. PicksCorrectlyIncorrectlySHARE Preseason AP67426571.8% RPI72332269.1 Game outcomes picked Outcomes for preseason polls don’t count any games in which both teams were unranked.Sources: The Power Rank, AP, ESPN Preseason coaches67126371.8 read more

4 Tips for Harnessing the Power of Big Data

first_img Free Webinar | Sept. 9: The Entrepreneur’s Playbook for Going Global 3 min read No matter what kind of startup you run, analytics will almost always be crucial. This data will allow you to track product and service adoption, identify customer needs or product issues and communicate the progress to the outside world (like investors and other influencers).When I founded Travefy, a tool that simplifies group travel, we relied heavily on data collection from the outset. Knowing that our product requires users to go through a multi-step process — a scenario bound to intimidate or scare off more than a few potential customers — we’ve dedicated many resources to ensuring that we retain as many customers as possible, along with their info.Related: How the Young Founder of Apprenda Landed $16 million from Albany, N.Y. Here are four quick tips for making data collection and decision-making a part of your startup:1. Define what you can actually report. Before you even determine what data to collect and report, you first need to spell out the possibilities. What are the data points you could actually collect? While there’s a multitude of information to be found by analyzing user habits, if there’s no way to collect it, then you can’t worry about it. If you’re not developing an in-house data system, Google Analytics, Mixpanel, and KISSmetrics can help you make these sorts of determinations.2. Establish what reportable data is most important to your business. Make sure you think through what data points are vital to tell the story of your business. If you have an account-driven business, you’ll want to track the number of accounts. If you have a process-driven business, like Travefy, how many users get through each stage is probably your most important data point. So, sit down with your team and go through all of the available data to choose the best ones to accommodate your business. For some great ideas on how to communicate this data rich information, I recommend reading through anything written by data scientist Edward Tufte.Related: The Biggest Trends in Business for 2013 3. Review this data constantly and learn from it. Now that you have your data dashboard, you need to be continuously studying and discussing key lessons from the collected data. This is a vital step, which will teach you and your team about the way customers interacts with your product. It will also provide insight as to any key changes to your product that will enhance the user experience. Early on at Travefy we saw a big drop off when users were asked to vote for their favorite trip. Investigating further, we were quickly able to recognize and fix a user-understanding issue that only became apparent through testing.4. Repeat and change things up. Be flexible with some of the data you track to make sure there are no big underlying trends you’re missing. The data you’re collecting isn’t static, so your approach shouldn’t be either. One great tactic we’ve heard is to have one extra data point in your analysis or dashboard that changes on a monthly or quarterly basis.How do you take advantage of data in your startup? Share your tips in the comments section.**Apply Now** Are you an enthusiastic college- or graduate-student entrepreneur, eager to share your on-campus experiences? Apply to be a YoungEntrepreneur.com College Treps columnist. Opinions expressed by Entrepreneur contributors are their own. January 15, 2013 Growing a business sometimes requires thinking outside the box. Register Now »last_img read more

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