Return and Improve Model - 2019 Edition

One of my pet projects for numerous year is back for another go-around. 2018 wasn't kind to the R&I model, and if I remember correctly, 2017 fudged a lot of the probabilities (I honestly can't remember, so here are the links if you are interested: 2017 and 2018). If you are unfamiliar with the tenants of the R&I model, the 2017 link will explain it in great detail. For the sake of time, I will be as brief as possible. The R&I model looks at the percentage of multiple statistical categories that a team returns from the previous year's team. It then forecasts the probability of the current year's team improving its tournament performance compared to the previous year's team based on the return-percentage. Since the model seems to have less and less applicability in this current era of one-and-done college basketball, this model and its probabilities have not been "qualified" or "scaled" based on any extenuating factors, such as critical match-ups, seed differentials, or era. Let's see what 2019 holds.

2019 Quality Curve Analysis - Final Edition

Well, the bracket has been released, the match-ups are set, and now it is time to see how the next three weeks will likely play out. If you have read the previous three editions (and if you haven't, here are the links to three very good reads: Jan, Feb and Mar), you will recognize the chart below. It is the Final 2018 Quality Curve.

From the looks of it, there's a lot to talk about, so let's see what we can learn.

Bracket Profiles

After finishing the two articles on the seed-group loss table (Links to both: Part 1 and Part 2), I wondered if there was other data points that could be used to build a tournament profile. The SGLT presented a loss-share percentage (the L% stat) for seed-groups 1 through 12 and a seed-group loss-percentage against non-tournament teams (the N/L% stat). For this article, I want to present two additional data sets for returning teams. You can think of these two data sets as a return-and-improve model without the focus on the "-and-improve" stipulation. So if you like data-driven articles, you will love this one. If you're interested in bracket-picking models and pattern analysis, you may want to skip this one. With that warning out of the way, let's dive right into it.

Returners by Seed-Group

As the heading would suggest, this data set looks at the tournament field by seed-group for a given year and tallies the teams in each seed-group that went to the previous year's tournament. For reference, this count only looks at the Field of 64 (teams that fail to advance out of the First Four games are not counted as tournament returners).

Since I've only been looking at this data for about two weeks, I haven't had the time to fool-proof my excel formulas to precisely see how significant returners are to seed-group performance. Instead, I'll focus only on the areas that I think bracket-pickers would want to know: The seed-expectations for 1- and 2-seeds, and the R64 upset-potential of 5- and 6-seeds.

1. Since the data doesn't count First Four losers (First Four began in the 2011 tournament), let's start there. I find it very ironic that the addition of four teams into the field hasn't improved the totals of returning teams. Only two years from 2011 to 2018 have produced more than 33 returning teams. From 2002-2010, six of the nine years in that span produced more than 33 returning teams. It could be a contributing factor to the relatively sanity of those nine years when compared to the eight years of the First Four's existence.
2. The pattern that immediately catches my eye is the 1-seed returners. There are only three years (2014, 2010, and 2006) in which one of the 1-seeds didn't go to previous year's tournament. Those three years produced some of the wildest tournaments we have seen. When taken into the context of the groupings in Point #1, 2006 and 2010 produced the highest M-o-M ratings in the 2002-2010 era, and 2014 produced the highest M-o-M rating of all tournaments in this chart. 2006 failed to produce a 1-seed in the F4 (the other two only produced one each). Think logically about this: If a team can fail to qualify for a tournament in one year and then achieve a 1-seed in the following year, the specific team probably did so against a weak field in the latter year (which is indicative of a crazy tournament). All three of those 1-seeds that failed to reach the tournament in the previous year (UVA, UK, and MEM) bowed out before reaching the F4 (seed expectations for a 1-seed), and two of those teams had the same head coach.
3. As for 2-seeds, there seems to be only two outliers: 2002 (one returner) and 2006 (two returners). Ironically, both years produced a 2-seed in the F4, which happened to be returners. Both years also saw a 2-seed fail to reach the S16, which happened to be non-returners. In a even stranger twist, 2002 with its sole returner produced three 2-seeds in the E8 (which matches seed expectations for 2-seeds). Looking at their first-round counter-parts, the only three years (2012, 2013, and 2016) in which a 15-seed knocked out a 2-seed had zero returners for the 15-seed group. This makes 15-seeds 3-for-6 when none of the teams went to the previous dance with all other years producing at least one 15-seed returner.
4. Tournaments typically produce anywhere from two to four returners in the 5-seed group. Only three times (2015, 2009, and 2007) has a tournament had less than two returners, and contrary to logical assumptions, two of those three years (2007 and 2015) produced four 5-seeds advancing past their historical nemesis (12-seeds).
5. 6-seeds typically have two or three returners in their group. Of the four years (2015, 2013, 2007, and 2004) when 6-seeds produced one returner, they won at least two of their match-ups against their 11-seed counterparts. When the tournament reached historical levels of sanity in 2007, the only two R64 upsets in that year happened to 6-seeds.

Returners by Tournament Performance

The data set below presents a tournament's profile by counting the number of teams from a previous tournament's round if they qualified for the current tournament. For example, the 2018 tournament featured the 2017 National Champion (NC), the 2017 National Runner-up, only two F4 teams (which happened to be the NC and NR of 2017), six E8 teams from 2017 (again, the only two missing are the two F4 teams that failed to return), and eleven of the S16 from 2017 (the two failed-returners of the F4 and three teams that made the S16 but lost their next game).

While I haven't had time to dig deeper into the patterns of this table, I would definitely claim this data set to be the oddest of the two. For example, National Champions have returned to the tournament in the following year every time except on four occasions. In two of those four "non-returning NC" years, at least three 1-seeds made the F4. In the one year in which the previous NC and the previous NR did not return, it was the same year in which all four 1-seeds made the F4. I'm certain it is 100% coincidence. While I doubt this data set will turn into any useful bracket-picking model, I think it may be useful in formulating bracket-picking strategies to win your office pool instead of "How to get every pick right" like this blog aspires to do.

Post-Selection Show Addendum

Before you look at the chart below, one detail must be noted. Even though AZST and NCCU have play-in games in 2019, neither team won their play-in game in 2018. Since this method only looks at the field of 64 for year-to-year consistency, it does not matter what they do in their 2019 play-in games as neither was in the field of 64 in 2018.

I will state again for the record that I have no idea what this means or how to use this information as I have yet to fully back-test this method. For comparison purposes, the 2019 tournament profile looks a lot like 2004, except it has two less returning teams and one extra returning E8 and S16 team.

I hope you enjoyed this venture into tournament profiling. Even though we find many similarities between tournaments and the participating teams, it does seem like no two years are the same. These two profile sets seem to confirm that no two tournaments are the same since no two profiles are the same. As always, thanks for reading my work, and next Sunday starts Bracket Crunch Week. I just hope I'm ready and prepared for 2019!!!

2019 Quality Curve Analysis - March Edition

Well, the calendar has rolled over to another month, and at PPB, it means it is time for another installment of the Quality Curve analysis, so let's see what's changed in the last month.