Experienced Talent Model for 2021-2022

Back in 2018, I created a conceptual model to evaluate tournament quality. Throughout the blog, I've loosely used the phrase "experienced talent" to conceptualize a rare quality in college basketball that identifies potential national champions if a team possesses it and identifies chalkier tournaments (top-seed advancement) when many tournament teams possess it. This model, purposefully labelled the "Experience Talent Model," aims to identify teams and seasons with this quality. (NOTE: If you would like to fully understand this model, click here to read the primary article, and the sections labelled "Data & Methodology" and "Assumptions" should bring you up to speed.) This article will bring the E.T.M. up to the 2022 season with some interesting new insights.

Note on Methodology

For the years 2021 & 2022, I used an experience value instead of an eligibility value. The reason for the change was the similarity of the model's results for 2018 and 2019, even though those two years were drastically different in quality. Players get credit for either 0, 0.5 or 1 year of experience based on games played, usage (avg minutes), or production per minute. In the older years (2016-2019), the experience value was based on the eligibility status (Fresh=1, Soph=2, Junior=3, Senior=4). At the current moment, there are about 40 players across the freshman and sophomore classes that qualify at the 0.5 experience value. This amounts to a reduction of about 800 total ETM points for the year 2022 (the Total Value for 2022 would be 800 pts higher if these players were qualifying for full experience). For the time being, this 800 points is probably insignificant in a total of 26,000, but it is worth noting.

Overall Results

Let's see where 2022 ranks with past years.

As I've thought, 2022 has more experienced talent (3,700 ET more) points than 2021, which forecasts a more chalkier tournament than 2021 (I'd probably quit PPB if tournaments become more insane than 2021, especially that level of insanity with a year-after-year consistency). For perspective, if every Top 100 player stayed all four years with zero red-shirting, the ETM would give a reading of 50,500. The current reading is a little better than half of the maximum ETM. The rest of the information cannot be completed until the bracket is revealed.

Predictive Results

One of the more interesting results produced by the ETM is its pre-season predictive power. This is the Top 25 ETM teams for each of the last five seasons, and their tournament performance for that season (NOTE: PIL stands for Play-In-game loss).

Here are the quick facts: 

1. The National Champion is among the Top 25 ETM teams except for 2021 (Baylor, more on this in the appendix). 
2. Every year except 2017 had a Top 25 ETM team lose in the play-in game (maybe having to do with the sheer quality of 2017).
3. The Top 14 ETM teams (aka - the ETM lottery teams) typically have 3-4 teams not make the tournament that year. 
4. The bottom 11 ETM teams typically have more varying results, anywhere from 3-6 teams not making the tournament (and this more correlates with tournament quality as more ET teams missing meaning more insanity in the tournament).
   
5. Less predictive reliability, but I thought I would point it out anyways: The highest ETM team has never won the title, and three of the five have failed to make the S16. (NOTE: I would avoid using this model for predictive functions period.)

So how does 2022 match-up?

If we apply the trends of the last five years, this would be a safe assessment of the 2022 tournament participants. Of the Lottery ETM teams, these look most likely to miss the tournament based on early season bracketology in combination with advanced metrics rankings. North Carolina has looked wide-variance in its performance, but most important is their inability to protect the paint on defense. As bad as they have looked at it, Oregon has looked even worse at it suffering two 20+ defeats already this season. Miami and Louisville have bad losses early in the non-conference, and even worse, they have to compete for few tournament berths coming from another down year in the ACC. Last year, the ACC sent 7 teams with the highest seed earned being a 4-seed. I don't see eight from this conference, so there will be twelve teams fighting over seven (possibly six due to depth of other conferences) berths.

Of the 15-25 ETM teams, I've identified the four teams with the shakiest prospects of a tournament berth. Arizona State is already 2-5 while Washington is 4-4, and they go up against a top-heavy Pac-12 (UCLA, ARI, USC) and berth-competitors in ORE (each x2 vs their fellow Top 25 ETM team), Utah, Colorado, and Stanford. Mississippi State has no quality wins so far, and their one loss was a blow-out to LOU (also on this list competing for a berth). Georgia Tech has no quality wins, a bad home loss opener to Miami-Ohio, and competes in the ACC. Their one saving grace may be a string of home games in December against WISC, UNC, LSU, and USC (only WISC is not a Top 25 ETM team). This will not matter if they go sub-.500 in ACC play, which plays a 20-game conference schedule.

Last but not least, given that the ETM for 2022 shows an ET Total close to 27,000 with another potential 800 to be added to the total, it's another piece of the puzzle that suggests a calmer year in 2022. Assuming this holds to the end of the season, I would estimate around 8-9 upsets and an M-o-M rating around 12-15%. We'll see as the season progresses, and I'll probably put the final counts in a later article instead of coming back to this one and updating it. As always, thanks for reading my work and I'll be back in a few weeks with my next article.

Appendix

I've spent a lot of time looking at the rankings and the values assigned by the ETM after BAY won the 2021 title. If you read the previous article, which details the methodology of the ETM, the one aspect of the data that troubles me is the Interval Scale. Essentially, the 100th ranked player is only worth 4 total ETM points as a senior, which is essentially negligible. Baylor's Jared Butler was ranked T-95th in his class (which was actually ten spots lower than his teammate and bench-player Matthew Mayer), so by his Junior year (last year's title run), he was only worth 18 ETM points (and thirty points lower than Mayer). This was also the case of 2016 F4-team OKLA, who spent the entire season in the Top 8, but didn't have a single player in the Top 100 of any high school class ranking. These results made me rethink the value scale for Talent. Instead of the highest-ranked player being worth 100 points and the 100th-ranked player being worth 1 point, the talent scale should be condensed by a factor of 0.5 so that the highest is worth 75points and the 100th-ranked is worth 25 points. The scale would still be interval data, but ratio analysis would have a little more significance since the talent of the best would only be worth 3x as much as the 100th-best instead of 100x as much.

Opening Week Thoughts

Like I promised to my readers, here's my early intuitions about the 2021-22 CBB season.

Overall:

1. I expect the 2022 Tournament to be a lot more saner this year than 2021's record-setting insanity. For the sake of recency-bias, look closer at the 2015, 2017 and 2019 standards. These produced M-o-M ratings in the range of 10-12% and upset counts in the range of 6-10. We'll figure more of this out as we get closer to BCW.
2. I think the biggest impact on the current season is the 5th-year rule. As such, experience will be more abundant in 2022 than usual. With many of these 5th-year seniors using the transfer portal, I expect offense to be better than normal in the beginning of the season and defense to improve over the course of the season as teams gel together with their new parts (Throughout the article, this will be denoted as 5YOD to avoid typing it out so many times). Typically, it's the other way around as most teams start out strong defensively due to roster continuity and offense improves over the course of the season with experience being gained. I'm not sure how I feel about 2022 teams who are bucking the strong-offense/improving defense trend, so I'll put them on alert (ILL, HOU, TENN, AUB, FLST, MIST, MARY, FLA, UVA).
3. Haves vs Have-Nots: If I had to describe the quality of this year's tournament, the haves and the have-nots is probably the best description. Last year, the 2021 QC had a rising tail, and I believed this to be significant in the quantity of upsets and the stage of upsets (upsets happening in later rounds). My theory for 2022 states that the quality in 2021's tail will be pulled to the front of the curve in 2022 (thanks to the 5th year transfer rule), resulting in a calmer and chalkier tournament.
   
4. Coaching Changes. Another subtle impact to the 2022 season will be the number of basketball teams that have a different head coach than 2021. Of the 358 Division I schools, 57 have new head coaches (approximately 1 out 7). I don't really follow coaching changes too closely on a year-to-year basis because programs with new coaches rarely make the tournament. In a year where continuity is lacking, stability at the top of the program (i.e. - the head coach) keeps growth and development on pace so that tournament success is not hindered. When a team has to learn to play together with all of the newly added pieces, it can be the difference between a deep run and an early exit. When a team not only has to learn to play together but learn to play together in a new system, it could be anything from a breath of fresh air or thermo-nuclear implosion from the inside-out. Of the 57 coaching changes, here are my Top 10 in alphabetical order that I would keep track of because of tourney implications: Abilene Christian, Arizona, Indiana, Loyola-Chicago, Marquette, North Carolina, Oklahoma, Texas, Texas Tech, and Utah State.
   

Specific:

1. KU vs MIST: This is not your typical KU team. KU looks like a 5YOD team when traditionlly they are defense-strong early season and the offense comes along. It bothers me a little for their tourney chances because I feel like they traded away a lot of defense for a slightly better start to the season on offense. I don't think McCormack is one of the elite post players in 2022 and I know he's not a rim protector like past KU big men. KU will rely heavily on guards Martin/Agbaji, and they don't feel like the Mason/Graham duo that went to two E8s and one F4. Plus, most of KU's success in the tourney under Bill Self has come from a 1-seed (only once did they win multiple tourney games as a 2-seed in 2012). MIST is clearly offensive challenged. Julius Marble was the best post player in this game, which speaks volumes about KU's experienced bigs, but as the season goes along, I think teams will double-commit on him until a guard from MIST can prove to be a shooting threat. MIST does have a favorable schedule, but their lack of offensive power will be their Achilles heel until it improves.
   
2. DUKE vs UK: This DUKE team compositionally and statistically reminds of the 2018-2019 version except less talent and more experience. That team had freshmen Zion, RJ Barrett, Reddish and Tre Jones (the younger Jones brother) who were responsible for 77% of the season's scoring. This team has freshmen Banchero and Keels with guards Moore Jr (junior) and Roach (soph), all of which account for 78% of the team's points. Banchero reminds me of Kevin Durant (the college version, with potential to be like the NBA version). Keels reminds me of a smaller Zion except less ability and less upside. Moore Jr can run the point comparable to Tre Jones, but this hugely diminishes the impact of Keels because he's not as effective playing off-ball. This team should be able to protect the paint like the 2019 version with veteran big-men Williams and John. Between the six of them and senior role player Baker, they are a complete team, unfortunately you can only play five on the court at one time. Their season may come down to finding the right five out of seven that wins the match-up, and hopefully Coach K's farewell tour won't distract them from the team-development they need. UK in its current state looks like a team of specialists. Tshiebwe is an offensive-rebounding machine, not surprising since he transferred from WVU (a consistent quality of a Bob Huggins team). Kellan Grady and Davion Mintz are 3pt specialists, not surprising since they came from Davidson and Creighton, respectively. Sahvir Wheeler is a play-making point guard, not surprising since he spent two years at Georgia under former IND head coach Tom Crean. With Keion Brooks being a roll-man in pick-n-roll, TyTy Washington being a combo guard, and Toppin/Collins being interior anchors, UK has the pieces of the puzzle, but putting five specialists on the court and playing as a cohesive team are two different things. Their 2018-19 unit had one developed piece in soph PJ Washington, which means that unit had to develop both individually and collectively to reach the E8 in a relatively strong year. This team has more developed pieces and desperately needs to develop collectively, especially their team defense. If they can, they could match or even surpass the success of the 2018-19 team.
   
3. UCLA vs NOVA: This was arguably the best game to watch during opening week. I feel with at least 95% confidence that I saw two F4 teams in this game. UCLA started strong out of the gates, which is typically the case with the home team. They protected the paint 1v1 exceptionally well as NOVA tried to post-up perimeter mismatches. This did lead to some foul trouble for undersized PG Tyger Campbell, but UCLA has plenty of options to run their offensive sets. Where UCLA struggled was defensively on in-bound sets. NOVA scored much easier in the paint on these plays than 1v1 isolation in the post. NOVA looked exceptional on the perimeter, not surprising since Jay Wright emphasizes 3-pointers and free throws in this era of basketball. Where NOVA struggled was their decision to switch all ball-screens. As experienced as NOVA's guards are, I don't think they are capable of defending isolation against UCLA's perimeter players. UCLA's Tyger Campbell had an inconsistent and foul-plagued first-half, but when NOVA began switching everything defensively, he abused 1v1's against NOVA's big men. Both teams gave up multiple 10-point advantages in this game. UCLA led by nine in the first-half, only to be tied before the last possession of the first-half, then NOVA led by ten in the second-half, only to miss the final shot of the final possession to go to OT. Then, NOVA lost the OT period by nine points. All in all, I did not like NOVA's gameplan offensively or defensively against UCLA. Granted, it is what NOVA does best and they will beat a lot of teams this year with it, but UCLA is not the match-up for it (nor GONZ for that matter), so if these teams meet again, hopefully NOVA can do something different.
   
4. GONZ vs TEX: If the 2022 tournament was played in Nov, I would have no problem slotting GONZ as my third F4 team (with UCLA, NOVA, plus one). However, the tourney is played in Mar and GONZ's path is entirely different than the rest of the country, and it may be why their path has never ended in a title. This season, GONZ lost more than 50% of its scoring from last year's runner-up team, so it feels like a rebuild year for them even though they are miles ahead on development than other teams right now. It would be hard to deny them a S16 run, but this team looks nothing in terms of total team quality like its 2017 and 2021 counterparts. GONZ does play UCLA in a few weeks (I'm leaning UCLA to win at the moment) with top-overall seed in the tournament potentially on the line. They also have games against DUKE two days later and ALA a week later. TEX looked like a veteran-laden team that has never played a game together. Another 5YOD team with a brand new coach, they have four months to find their chemistry and cohesion, especially defensively, if their tourney run is going to justify their pre-season #5 ranking. My biggest worry with TEX is their inability to win big games. Collectively, this team has two NCAA wins on their roster, both from Creighton transfer Christian Bishop (Marcus Carr redshirted the year MINN won their 7v10 matchup). I wouldn't count out incoming coach Chris Beard from TXTC, but if I were coaching the team, I'd figure out my starting guard trio first (Carr at PG, Jones at SG, and Febres at SF with Ramey as 6th man) and run with it for the whole season.
5. FLA vs FLST: I'd like to say this game was a tale of two Florida teams, but it looked more like five or six different Florida teams. When FLA was moving the ball in their half-court offense with the pass, feeding the post-entry to Castleton, and kicking out to shooters on the opposite side, they looked crisp and clean. When they tried to run dribble-drive action, it typically resulted in a turnover, blocked shot, or otherwise low-percentage shot. I also don't understand why they were full-court pressing FLST. If there is one team in the country that wants to play in the open-court, it is FLST, especially this youthful and less-talent-than-usual FLST team. FLST repeatedly punished FLA's full-court press. In the 2nd half, the tables flipped and FLA begin locking down in the half-court while FLST began to extend full-court, and in predictable fashion, the game became a FLA runaway win. Despite his consistency in winning NCAA tournament games, I am highly skeptical of FLA HC Mike White. There are times the camera gives him a close-up and he looks like a deer in headlights. Not to mention, the full-court press strategy should have never been on the drawing board for this game, and it should have been called off after FLST broke the press twice for baskets and eventually a lead in the first half.
   
6. Honorable Mentions: Thanks to the B10 Network showing replays, I've been able to catch a lot of teams in this conference. MICH and PUR both look like teams that will guarantee you a win in the tournament. I have a hard time seeing PUR lose another opening round game two years in a row, and consistent-scoring big men like MICH Dickinson is usually a safe bet. MARY looked incredibly shaky against George Washington. I don't know if this team can pull off a tournament win like last year's squad, but they have to prevail through conference play first. Finally, OHST scares me. They have good players and good chemistry, but I think they're upside is limited and they're missing guard-play and guard-depth. Their core three (Sueing, Young and Liddell) are all forwards/centers, and their truest shooter (Ahrens) is a defensive liability against opposing 1s and 2s. If they pair in the tourney with another perimeter-oriented team like 2021's match-up against OROB, they'll be going home after one game in 2022 as well. Fortunately for their sake, the B10 is post-oriented this year, so the conference match-ups will be to their liking.

My Final Thoughts

You may or may not find anything in this article useful or agreeable. The one thing I'm taking away from it is self-reflection. This gives me an opportunity to record my thoughts and perceptions (nicer terms of describing my biases). When I get to BCW, I don't want my biases clouding my bracket analysis, and knowing my biases is probably the best way to approach it. After all, you can't solve a problem until after you first identify it. Maybe I've accidentally stumbled onto something. Anyways, thanks for reading my work and I'm most likely to produce an article on the Experience Talent Model even though the schedule says it is coming much later.

Welcome to the 2021-2022 College Basketball Season

Welcome back to the greatest challenge of all time. And what a season we just finished!!! I don't think that seven-word sentence truly captures what we witnessed, but I've got an entire article (well, maybe half) to expound upon it. I also want to get some go-with-my-gut predictions for this season, so there's a lot to take on without writing an entire book.

2021 NCAA Tournament and Lessons Learned

The 2021 Tourney was one for the record books. It produced a 23.46% M-o-M rating, an all-time high surpassing 2014's mark of 21.35%. It also produced a record-tying 15 upsets, matching 2014's count. The PAC-12 came out of purgatory with three teams in the E8 after barely getting three teams in the field in the previous two tourneys. It also produced five new coaching contenders: Andy Enfield (USC), MICH (Juwan Howard), Mick Cronin (UCLA), Eric Musselman (ARK), and Wayne Tinkle (ORST). Since 1985, only two coaches have won National Championships without reaching the E8 in a previous season (a nice little homework assignment if you don't already know the two).

In the Final Results article that I published at the end of last season, I said "the high-rising tail of the QC was the key to understanding the outcomes of 2021." For most of the season, the QC remained constant in the #26-#50 positions. Most of the fluctuations occurred among teams in the upper-half (#1-#25 positions). From the time of the February Edition (Feb 3) to the Final Edition (Mar 14), the entire QC had shifted upward along all parts of the curve (yes, with the exception of a few spots). I theorized in the Final Edition that the high-rising tail was probably the result of these teams "catching-up" to their true potential, and I thought this way because the efficiency improvement wasn't coming at the expense of the upper-half of teams. When the tail of the QC is elevated, it portends a higher quantity of upsets in the R32 and S16. It simply means these teams are playing better and the difference in quality between them and the upper-half is thinning. In simple terms, there would be very little difference between team #24 (a true 6-seed) and team #44 (a true 11-seed), and if you catch a weak 3-seed, you have an 11-seed with S16 likelihood. I understood exactly what the QC was telling, but where I failed was application (more on this in a later section).

I also stated in the Final Results article that Seed-curve & Seed Displacement were not as important to the outcomes of 2021. The seed-curve approach takes strength and weakness in the SC compared to the QC and projects over-performance and under-performance accordingly. I explained why it can run into problems with the 1-8-9-16 groups. 1-seeds, 8-seeds, and 9-seeds were all expected to out-perform their expectations, but this is completely impossible when all three groups play each other in the first two rounds. The hypothetical maximum is 16 wins for 1-seeds (in line with expectations instead of over-achieving: 4-0 in R64, 3-1 in R32, and the remaining three advancing to the F4), 3 wins for 8-seeds (1-3 in R64 with the lone winner advancing to the E8), and 3 wins for 9-seeds (3-1 in R64 and 0-3 in R32). It's a near-perfect dream scenario with very low probability of happening. The seed displacement approach uses over-seeds and under-seeds to predict outcomes. Under-seeded lower-seeds should defeat over-seeded high-seeds. When it is really obvious, it works, but these situations are few and far between. When it appears like it works, it is nothing more than a coin flip. For example, WISC and LOYC were the largest under-seeds in 2021 (They were as strong as 3-seeds but received 8- and 9-seeds). Both won their R64 games with relative ease and met accurately seeded 1-seeds in the R32, but only LOYC pulled the upset. This method should not be 50-50 if it is practical (either both should win or both should lose). For the record, I've come to rely less and less on this approach for individual games. I'm far more comfortable with over-seeds and under-seeds being mapped into the bracket to find pods/octets/regions with upset potential, and even this approach is still under development.

One lesson I learned from the past season, but will probably never get a chance to apply to future seasons is the impact of no home-court advantage. For most of the season, fans were prohibited from attending games (depending on the state laws and restrictions of the two teams, and yes, some restrictions were lifted near the end of the season). This lack of home-court influence meant road teams were more likely to win if they were the more skilled teams. Even advanced metrics ratings systems adjusted their margin of error for home-court advantage. For example, Sagarin ratings was using a 2-pt margin of error for home-court advantage when other years it was usually 3- to 3.5-pts. When it comes to the NCAA tournament, least distance traveled to site locations is a key factor in wins and losses historically, which is why the Selection Committee tries to give closer-to-home sites to the higher seeds. In a year with no home-court advantage, distance traveled might be an incentive instead of a detriment. You have more to lose if home is further away from Indianapolis. The E8 aligns with this notion: GONZ (Washington state), USC and UCLA (California), HOU and BAY (Texas), ARK (Arkansas), and ORST (Oregon), with MICH (self-explanatory) being the lone exception. I thought teams closer would have an advantage, which is why I liked 4-seed PUR (Indiana), and they didn't even win a game. Most of the close-by teams lost early: 1-seed ILL was the first to go home (albeit to an Illinois-based LOYC, who ended up losing to ORST), 2-seed OHST (Ohio) failed to win a game, 2-seed IOWA lost to ORE , 3-seed WVU and 5-seed TENN are a stone's throw from Indianapolis but lost to further-traveled teams in 11-seed SYR and 12-seed ORST. My bracket probably would have scored higher from this strategy than curve-fitting.

Grading the Predictor

Failure of Application: Earlier, I mentioned I correctly read the QC but failed to apply it. When the QC was predicting a higher quantity of upsets in the R32 and S16, I should have looked to round-by-round upset history to see exactly what "higher quantity of upsets" means. On average during the advanced metrics era (2002-present), the number of upsets is 2.83 upsets in the R32 and 1.17 upsets in the S16. When you factor in recency bias, the averages are slightly higher. In other words, a higher quantity of upsets in these rounds would mean 4-5 in R32 and 2 (maybe 3) in S16. Though I never made any predictions public, I went with a 4-2-1-0-0-0 upset-by-round model for my own bracket. A failure to properly apply knowledge and insights produced this failing grade: F.

Seed Guides: All in all, I give a B+ or B. My seed-group analysis was pretty spot on, but far from perfect. I'm not going into full detail on this since there is an entire article you can check for yourself. The one glaring miss keeping me out of A-range for a grade is my comment on the 15-seed group when I said "Just like the 16-seed group, nothing to see here." That was a huge oversight.

Meta Analysis: I did try to apply meta-analysis to my personal bracket. I favored teams with the trading strategy (Top 50 2P% Def with either a higher rank in EFG% or a higher 2P% rank). It's why I had BYU making an E8 run. I also mentioned in the article that ORB was an anti-meta play as long as they fit some of the other anti-meta criteria. This was the gold mine call. Three E8 teams (BAY, HOU and USC) featured Top-10 ranked teams in ORB%. All teams in the E8 featured Top-128 stats in ORB%, and no other stat boasts this proficiency level (the next closest is TOR with all teams in the Top-150). ORB% may be in decline in college basketball, but BAY demonstrated how important it was in the NC Title Game. Their entire lead in the game due to 2nd chance points. Again, another B+ or B in these predictions, and this is pretty good considering how untested this approach is.

Gut Predictions: These were not perfect, but they were as good as it gets and they were insights you would not get from any leading sports news, stats or analysis outlet. First, all year I said the tournament was likely to feature a forfeiture. Of course, it was a 2nd-day match-up between ORE and VCU, which is why I missed it. I also stated that one fortunate team could get to play 5 games in Lucas Oil Stadium and one in Hinkle Fieldhouse. The consistency in venue can do wonders for shooting. BAY was the fortunate team in playing four games in Lucas Oil and the other two in Hinkle Fieldhouse. For a team that was very dependent and proficient on 3P%, I'm sure venues played a role. Unfortunately, the NCAA and CBS doesn't pre-release venue and tip-off time for all 63 games before the tournament starts (SOMETHING THEY SHOULD DO!!!!!!!!). Finally, I made a list of teams that preemptively traveled to Indianapolis. With the exception of GONZ, the rest of the list was Cinderellas, two of which advanced to the S16 from stunning upsets. 15-seed O-ROB won over both 2-seed OHST and 7-seed FLA, and 8-seed LOYC stunned 1-seed ILL. The only miss from this article was mentioned above about home-field advantage, but for this article I get an A.

Thoughts on the Upcoming Season -- Are We Back to Normal???

I worded it this way because it depends on how we define normal. Structurally, this season should be normal as we get back to full schedules and full venues, normal locations for tournament games, and normal mistakes by the selection committee for easy bracket picks. 

In terms of bracket sanity/insanity, I believe 2022 may be abnormal (and abnormal implies a sane/calm tourney). The 2021-22 season features a one-of-a-kind scenario for college basketball: The 5th-year player. Due to Covid-19 cancelling the 2020 post-season, seniors were granted a fifth year. I'm not going to debate the merits of it, but I will say it adds something that college basketball always seems to lack: Experience. As I've said many times, I believe experienced talent wins national championships (This article explains experienced talent). I do intend to look at an updated Experience Talent Model at some point in the season, but whether or not I get around to doing all of the research for it is another story. Back to the discussion, experienced talent adds stability (low M-o-M ratings and low upset counts) to the tournament because experienced talent features players who can play the game (talent) and understand all of its detailed intricacies (experience). Compared to last year's record M-o-M rating and record-tying upset count, I think this year will be a lot calmer with this retained experience. If you believe in mean-reversion like I do, that's another reason why a calmer tournament is expected.

One of my newest tools, the Seed-Group Loss Table, was not included last year. The tool implements winning percentages among seed-groups and uses these to project seed-group performance. When a season features game cancellations throughout the season, this affects the reliability of that season's win percentages. For example, GONZ and BAY were scheduled to play during the regular season, and this missing game affects the eventual win-percentage of the group, making its results less reliable and less comparable to other years. For a fun article this year, I may hypothetically attempt to project 2021 with this tool and see what the results would be, but it should be back for the 2022 tournament.

Not only do I get to take my newest tool for a spin this year, I should get to write more than just QC articles for this season. With games starting on November 9, I can't wait to get this season underway. As always, thanks for reading my work.

2021 Final Results

Sorry for the lack of post-BCW interaction (IRL/Work has been interesting). Anyways, I thought I would give a recap of my processes and my results for those interested. I will do a more detailed analysis of the 2021 tourney itself in my Welcome Back article in November (what a teaser).

2021 Meta Analysis

With it being late, I'm going to rapid fire this article. First off, I used the relative data instead of the raw data so that historical comparisons can be made. I will be comparing to the 2016-2019 tournaments like I did in the previous two meta articles. Second off, when I recommend meta and anti-meta criteria, feel free to head over to Bart Torvik's site and find teams that meet this criteria. Third off, I cannot stress this enough: This is untested and high-risk, but if you're like me, you want to be on the cutting edge of new. With that, let's see what 2021's Meta looks like.

Historical Relation of 2021 Seeds

Using the Torvik Rating system, which utilizes the familiar Pythagorean win percentage methodology of Bill James, I gauge the relative strength of the 2021 seeds to their counterparts in previous years. (NOTE: I believe Torvik may have tweaked his formula because some of the data in my spreadsheet made years ago are different from the percentages on his site. As a result, the data in this method may be inaccurate and predictive ability may be higher risk.) Remember when 2018 had historically weak 1-seeds and then one of them bowed out to a 16-seed. This is another attempt at this approach. This compares all teams of a particular seed group regardless of year. Since the Torvik ratings go back to 2008 (13 total years), this will rank all seeds from 1 to 52 (4 seeds per year x 13 total years). Thus, if a seed this year is ranked #1, they are the strongest seed ever among the thirteen years, and if they are ranked #52, I'd pencil them to be upset if you get my drift. (NOTE: 11-14 seeds have different totals because they are/have been play-in game seeds, so the total number of seeds is listed "out of ##" to denote this distinction.)

2021 Quality Curve Analysis - Final Edition

I'm not going to bore you with catchy intros and wordy mantras, so let's see the final edition of the 2021 QC and the 2021 seed curve.

Late Night Thoughts

There's a lot going through my mind with the Selection Show less than 15 hours away. I've spent most of this week thinking about the operation of the tournament and how the operation of the tourney can impact a team's performance in the tourney. Here's what I've ran through my mind on numerous occasions.

Another Theoretical Attempt at Meta Analysis

I received a lot of feedback on the meta-analysis article, and since I had some spare time this week with work being slow, I did some further digging/torturing into the data. The first attempt looked at meta through raw data (or more specifically, raw averages). It showed how the national averages of 2016-2019 are mostly similar to one another but different to other years because rule changes created different metas between all the years. What if there was a way to create cross-meta comparisons so that the data in the years from 2008 to 2015 becomes more relevant to the data of 2016-2019?

A Theoretical Attempt at Meta-Analysis

If you've ever played trading card games, then you'll have same familiarity with my current topic. I grew up playing all kinds of card games, but trading card games were always my favorite because of the diversity in how you could play them. You could be aggressive with quick, hard-hitting strategies, conservative with control and lock-down strategies, or clever with endless-looping combinations that "break the game" and result in an auto-win or a special end-game format to handle the "broken-game condition." The variety of different approaches results in a competitive state labelled as "Meta", where X% of the decks are Strategy A, Y% of the decks are Strategy B, Z% of the decks are Strategy C, and etc. For example, if you attended a tournament and expected X + Y > 60% of the meta, you would want to construct and play an anti-meta deck, which means your deck would beat "on average" more than 60% of the decks being played at the tournament. In more colloquial terms, it is akin to playing a "rock-type" deck when everyone else is playing a "scissors-type" deck (and hoping you don't randomly run into the less than 10% playing a "paper-type" deck).

College basketball demonstrates a meta-like quality, given the variety of offenses (passing motion, dribble-drive motion, swing, point-action reversal, shuffle, princeton) and defenses (pass-denial M2M, pack-line M2M, zone, and an occasional full-court havoc/hell press) in the game. What I am more interested in (and what this article will look at) is a statistical-based meta for the game and the tournament.

2021 Quality Curve Analysis - February Edition

I'm back with the next installment of the monthly QC Analysis. Let's jump right into it.

2021 Quality Curve Analysis - January Edition

I haven't written my inaugural article this late in the season since the very first season I started writing this blog in the 2015-2016 season. There's a lot I want to go over, so I'm going to keep the fluff to a minimum. I'll start by giving some of my insights on the 2020-21 season, then dive into the January QC, and conclude with my insights on prediction.