Post-Player Production of Past Champions: The Archetypal Approach (Part 2)

I'm back with Part 2 of my study on post-player production of national champions. If you have not read Part 1, it is not necessary to have read it in order to understand the concepts and/or analysis presented in this article. However, I feel it is a worthwhile read as a contrast to this article, and I may make a few fleeting references to Part 1 concepts in this write-up. Let's start at the same starting point as last article, and we'll move forward from there.

The table above is the National Championship Profile model with post-production filled-in and all other components left blank. The values are the same values from that article, but since these two articles are a rebuilding job, these values will be corrected at the end of this article. Also, the table only goes back to 2001 because of data reliability (I have data back to 1998, but I'm only confident in the data up to 2001, and it's the data which will be used in both articles). The archetypal approach is represented by the left column labelled POST, and it concerns the function of post-production as a part of the team's dynamic. In simple terms, we will be looking at the whole picture of the national champion and evaluating post production's share of the whole. Instead of the O (Offensive-oriented post-production), D (Defensive-Oriented post-production), or B (Both offensive and defensive post-production), new archetype names and labels will be created.

Similar to the first article, the hardest part of these write-ups is the organization/presentation of ideas. In Part 1, I originally wrote an article that felt more like a novel in length, so at the last minute, I decided to reorganize the paragraphs and the flow of ideas. This resulted in a three-day delay in submission, but it halfed the read time. Again with this article, the organization of ideas is a nuisance, but I think the easiest layout for Part 2 is to detail the parameters first, then unveil the archetypes with depth of analysis, and finally a general wrap-up to the concept with all remaining questions answered.

STEP 1: Parameters for Qualifying Players

Like I did with the taxonomical approach, I wanted to start the archetypal approach with an minimum level of participation or utilization. If a player doesn't measure above the utilization parameters, they are not counted as part of the team's rotation. I required qualifying players to meet two utilization parameters:

1. Play at least 10 minutes per game (I kept some players who averaged 9.8 MIN per game only because I'm not sure if it is a rounding error, and I kept one player on 2015 DUKE with less than 9.8 MIN per game because of a mid-season suspension to a rotation player and its effect on the MINs, FGAs and PTS per game), and
2. Have a shot distribution > X%, where X = (Rotation Position -1) x 0.4. If a player is ranked 7th in order of shot distribution, then his share of his team's shots should be greater than 2.4%, which can be calculated as (7 - 1) * 0.4. 8th would be 2.8%, or calculated as (8 - 1) * 0.4. 9th would be 3.2%, or calculated as (9 - 1) * 0.4. When I show the full ordered table in Step 3, you'll see more visually why this matters.

Shot distribution (ShDs) is just a shorter way to say the percentage of a team's shot attempts that a player takes. I also calculate Free Throw Distribution (FTDs) and Rebound Distribution (RbDs), which is the share of free throws and rebounds, respectively, that a player takes. These three metrics will play a critical role in defining archetypes and post-player function in these archetypes.

STEP 2: Team Archetypes and the Function of their Post-Production

In the original National Champion Profile article, I talked in the Intro about how 2021 BAY resembled the composition of 2019 UVA, but they were a more consistent variant of 2019 UVA in a weaker 2021 field. The resemblance of the two teams is why I added point guard production and post production to the National Champ Profile model. If I had went into far more detail like this reconstruction effort is doing, I probably would have made less errors in that model in the Final 2022 write-up. That is in the past, but I wanted to start with these teams as the first archetype, and by referencing my history on the subject, I wanted to document that I've been doing this well ahead of the ESPN half-time stats analysis.

Big 3 Archetype (B3): In the old notation, most of these teams were the D-archetype (Defensive-oriented post production). Now, they will be known as the Big 3 archetype, mainly because these teams have the same metrics in common with each other:

• With some player rearrangements (2014 CONN Daniels, 2016 NOVA Jenkins, and 2018 NOVA Bridges as SF instead of F), the Big 3 Scorers look like the PG-SG-SF roles and the remaining post-players as a Big 3 of their own in the post.
• The highest of the Big 3 scoring at least 15.5 ppg or at least 19.8% shot distribution.
• The lowest of the Big 3 scoring at least 11.8 ppg or at least 15.7% shot distribution.
  
• The drop-off in shot-distribution from third to fourth is at least 2.8% (2018 NOVA) but can be as much as 15.7% wide (2010 DUKE). Most of the 3rd-to-4th drop-off is in the range of 4.1-8.6%.
• The Big 3 Scorers all had 3P% >= 0.359, and for five out of six teams, they were also the Top 3 in FTDs.
  
• The Big 3 Post combine for approximately 18.5-22.3% of the ShDs (if we count 2018 NOVA as an anomaly), and this averages out to about 7% per post player.
  
• The Big 3 Post also combine for 3.0 - 7.5 ORBs per game, but no individual post-player averages more than 7.9 RBD per game.

Here are some other details about this archetype:

• All occurred from 2010 and later, which is one year after the 3-point arc was extended to 20'9". Also, five of the six occurred from 2014 and later, which is the year that the (horrible) Freedom of Movement philosophy was instituted. Imagine that, a rules philosophy dictating how a team should be built and how they should play in order to win the game.
• RbDs is not consistent among the six teams. For 2014 CONN and 2019 UVA, the Big 3 Scorers own a major share of the rebounds, which is counter to the other four teams in the group. For documentation purposes, I debated segregating these six teams into three archetypes along this discrepancy: 2010 DUKE and 2021 BAY as one, 2014 CONN and 2019 UVA as one, and 2016 NOVA and 2018 NOVA as one. I didn't like this approach because it gave the appearance as sorting by team/coach philosophy rather than metrics.
  
• 2014 CONN and 2018 NOVA are the obvious anomalies in this archetype. 2018 NOVA because they had the smallest 3rd-to-4th drop-off, which was a 2.8% difference compared to the other five teams in the group (4.1%, 4.9%, 7.7%, 8.9%, and 15.7%). Only one other team not in the B3 archetype had a 3rd-to-4th drop-off higher than 2018 NOVA (2017 UNC - 3.2%), and for reasons to be demonstrated in the next group, 2017 UNC and 2018 NOVA are not grouped together. There is a good chance, depending on who wins the 2023 title, that this group alone gets an overhaul in the off-season. 2014 CONN probably has the best match to the statistical mean of the B3 parameters, they don't match the statistical results of the group (RbDs is more guard-oriented, lacking a third post-player in the rotation, and highest BLK total by a longshot). If there was another group that 2014 CONN could belong, it would be the next archetype.

So, let's look at the next archetype group.

Go-To Player Archetype (Go2): In the old system, this group contains teams from all three labels (O, D, and B). Here's what these teams have in common:

• The leading scorer on the team took at least 23.7% of the team's shot attempts, and the next highest player took at least 6.5% fewer. The next-closest team to all of these parameters is 2014 CONN (as stated in the previous group) with only a 23.4% ShDs by the leading scorer and only a 3.8% drop-off from 1st-to-2nd.
• Three of the Go-To players were SFs and three were SGs (2014 CONN's Go-To was a PG).
• The Go-To player either had the highest FTDs on the team as well or had a 3PR >= 0.431 (the more jump shots a player takes, then the less likely they are to be fouled and get FTs).
  
• The two starting post-players on Go2 archetypes have at least 13.2 RBDs per game, with five of the six teams having one of those two starters with at least 8.2 RBDs per game. Looking closer at the 13.6 combined RBDs per game, at least 5.7 of these are ORBs. If the Go-To Player is taking more than 1/4 of the shots and missing more than 50% of their shots, there should be plenty of ORBs to go around. (Note: Since there is little difference in minutes played and shot distribution for 2003 SYR post-players, I just counted the post-production of the highest two producers).
  
• The next three highest in ShDs form a secondary Big-3 to the Go-To player, with the highest ranging from 14.5-18.5% ShDs and the lowest ranging from 11.8-14.6% ShDs. On every team in the Go2 archetype, one of the top in the secondary-3 is a post-player, and in four out of six teams, two of the secondary-3 are post players.
• Four of the six teams have post-production that could be described as a block-party, defensively speaking, as their total combined BLK is at least 3.7 BLK per game. Ironically, all four of these teams played before the Freedom of Movement rules philosophy, which turns legitimate blocked shots into fouls on the defender for the sake of artificially increasing box scores.

If there is one anomaly in this group, it would be 2003 SYR. For starters, it is one of three teams whose leading RbDs player is not a post-player (the other two are 2014 CONN and 2019 UVA). Unlike those two teams, the next three out of four in RbDs are post-players, so they can't be grouped with those teams. Likewise, 2003 SYR's Carmelo Anthony fits the description of a Go-To player, and they had a secondary Big 3, not a primary Big 3 like 2014 CONN and 2019 UVA. Their numbers could also be a function of being the only national champion on the list to play a zone as their primary mode of defense. The biggest differentiating factor is the ShDs drop-off from 4th-to-5th and from 5th-to-6th are the largest in the group, with 2003 UMD being the next closest to either of these drop-offs.

If there is another archetype that either of these two teams could belong, it would be the next one.

Two-Way Archetype (2W): In the old system, these teams could easily be classified as a B-archetype, although I think I settled on O-archetype for a few of them. Here's what these teams have in common:

• At least five players that each take at least 12.4% of their team's shots (in the case of 2012 UK, they have six players above the 13.8% mark in shot distribution, which almost breaks the bounds of mathematics to achieve and only the two NOVA teams come close to resembling it). With this many players getting this many shots, it is no surprise that each team has at least five players averaging 9.5 PTS per game and higher.
  
• Four of the five teams (2004 CONN) have each of their Top 4 in FTDs above 14.3%. Not to mention, three of their top 5 players in FTDs are post players (2012 UK is this exception).
• All five teams have their primary two post players collecting 40.5-54.9% of the team's rebounds. Not only is their top 2, but three of their top 4 in RbDs are post-players (again 2012 UK).
• All have two post players averaging at least 1.3 BLKs per game, and the entire post producing at least 3.2 BLKs per game.
• Four of the five teams have an even distribution of AST (only 2004 CONN having 11.5 AST concentrated to just two players).
  

The teams in this group are probably the most balanced teams to win a championship. The numbers show they can play offense and defense, both inside and outside, and with any player able to score, they should be able to take advantage of the opposing team's worst player. Both 2004 CONN and 2012 UK appear to be outliers in this group. For 2012 UK, it has a lot to do with being a 6-man rotation with a sparingly used post-player as a 7th in the rotation. It does a lot to explain why six players can achieve such a high threshold in ShDs. 2004 CONN is anomaly in its own right. They share similar stats with the Go2 archetype if not for the high ShDs of Okafor (2nd highest on team), only 2.0% less than Gordon. Likewise, 2002 UMD and 2003 SYR could be in this group if not for the wide drop-off in ShDs from 1st-to-2nd. 2004 CONN seems to have a Big 2 with a secondary Big 3 in ShDs (almost like a poker Full House Archetype), but this archetype is not shared by any other championship team. There are two other teams that are somewhat close to these metrics, but they do not share the same stat levels as 2004 CONN, or any team in the 2W archetype.

Let's look at these two teams and the next archetype.

5-Man Offense Archetype (5O): In the old system, these teams would have received the O-archetype. Here's what defines them, yet separates them from other archetypes:

• Five players that each take at least 13.8% of their team's shot attempts (1.4% higher than the 2W group) while each scoring at least 11.2 PTS per game (1.3 PTS per game higher than the 2W group) with one player scoring at least 20.8 (only 2004 CONN of the 2W group is close).
• Coincidentally (since two is a small sample size), both teams had their two primary post players collect 40.8% of their team's total rebounds. Both had four players each collect at least 1.7 ORBs per game, which amounts to an average of 6.8 extra shot attempts per game.
  
• Both teams only have one post player averaging more than 1.3 BLKs per game (unlike the 2W group which had two). The lack of interior shot blocking is compensated by five players averaging at least 1.0 STLs per game. With five extra shots coming in transition, it's easy to get at least 11.2 PTS per game while keeping your opponent off the scoreboard.
• Both teams have a concentrated distribution for AST, with the majority of AST shared between one or two players (2004 CONN of 2W had this trait and the ORBs trait, nothing else though).

With the exception of these schools being arch-rivals, the parameters and metrics tend to fit. In fact, only three national champs have players who PTS per game is more than their ShDs (as long as ShDs is greater than 6%), and two of those three teams are 5O-archetypes. Boozer took 13.8% of 2001 DUKE's shots but scored 14.0 PTS. Likewise, Tyler Hansbrough and Ty Lawson's PTS are above their ShDs figure. When PTS>ShDs, it suggest high efficiency in scoring (in other words, these players probably lead in points-per-shot-attempt among all players studied). Unlike their offensive prowess, they don't have the defensive stats to qualify as a 2W archetype, especially in BLKs. Likewise, they don't have a high enough difference in 1st-to-2nd ShDs (2.8 and 2.0, respectively) to qualify as a Go2 archetype. If there is one team in other groups that could resemble this 5-Man Offensive assault, it may be 2018 NOVA, but they lack the ShDs, FTDs and RbDs from post-production to resemble these two teams (probably a product of different eras of basketball).

Let's look at the last two teams and see what separates them from everyone else.

Post-Island Archetype (PI): They say that no man is an island, but these two teams had a center who  defy this wisdom. Here's what these two teams and their "island" have in common.

• A post-player who is 2nd in ShDs with at least 17.8% of the team's shot attempts, at least 25.7% of the team's FTDs, and at least 29.0% of the team's RbDs. If I remember correctly, the only other team that could qualify on this alone would be 2004 CONN (which is their 3rd possible group).
• Both teams also have all of their post-players in the Top 5 of their team's FTDs.
• Both teams also have all of their post-players in the top spots of RbDs (no post player is out-rebounded by a non-post player).
• The lack of shot-blocking is a peculiar feature, as neither center has more than 1.4 BLKs per game. This could be a product of their importance as an interior presence: By not taking risks of fouling, the BLKs stat is sacrificed in order to remain on the floor. (2004 CONN has BLKs).
  

Since 2005 UNC is the other team that features a player (Marvin Williams) with a higher PTS total than their ShDs, it is very likely they could transition to the 5O archetype (with some tweaking of the parameters of course). If another team were to win a national championship as a PI archetype, it would also make sense to move 2004 CONN to this group (with 2015 DUKE and the new team) and set tighter parameters to distinguish this group from all others (which means ensuring that 2005 UNC qualifies more as a 5O archetype than a PI archetype).

STEP 3: General Review of the Archetypal Approach

I would suppose that the best start to this review would be an overall view of the ShDs, FTDs, and RbDs to visualize how each archetype develops around these three parameters.

Each player is ordered highest-to-lowest (left-to-right) for each of the three parameters. To give perspective to post-production, post-players are marked with yellow boxes and small forwards are marked with green boxes. Notice how post-production is utilized by each archetype:

• B3: Post production is in the bottom-half of ShDs and FTDs, yet it is stretched across RbDs. In the B3 archetype, the post functions to defend the interior while claiming missed shots, both on offense and on defense.
• Go2: Post production is in the 2nd quarter (secondary to the Go-To player) and the 4th quarter of ShDs (reserves also taking on the supporting role), spaced throughout the FTDs, and heavily concentrated in the upper-half of RbDs. In the Go2 archetype, the post functions as a supporting role on offense (to take some of the scoring weight) while securing any misses by either team.
• 2W: Post production is two of the top 5 in ShDs, all in the upper 2/3 of FTDs, and mostly occupying the top spots in RbDs (usually one top spot occupied by the starting SF). In the 2W archetype, the post functions as 40% of the minutes/rotation and 40% of the scoring option (a balanced distribution), but still defend at a high level (BLKs not shown) and finishing off possessions with a rebound.
• 5O: Post production is two of the top 5 in ShDs, two of the top 5 in ShDs, and all in the tops of RbDs (especially when considered on the basis of per-minute played) with not as much help from the SFs. In the 5O archetype, the post functions as two-parts of a well-oiled, high-octane, 5-part scoring machine while dominating the glass on both offense and defense so that SFs can be more involved in the transition game rather than the half-court rebounding game.
• PI: Post production is occupies the 2nd spot in ShDs with very little drop-off from the 1st spot, takes 1/4 of the team's FTDs, and gets at least 10% more of the team's rebounds than the next post-player. In the PI archetype, the post functions as a one-man island (usually a center) whose presence on the floor dictates the team's will through high shot volume, high scoring potential (from the floor and the FT line) and high pursuit of missed shots.

Other details regarding post-production include

• Only two teams have a post-player as the top player in ShDs: 2009 UNC's Tyler Hansbrough and 2012 UK's Terrence Jones (big surprise that it isn't Anthony Davis).
• Two post-players in the top 3 of ShDs have only happened twice: 2002 UMD and 2005 UNC, both of which seem like eternities ago in college basketball (I wouldn't bet on a team in 2023 or the near future with this attribute).
• Only one team featured three post-players in the top 5 of ShDs (2005 UNC), and I would follow the same advice as the previous bullet point for this attribute.
  
• Two post-players in the top 4 of ShDs have happened four times, and three of these four were the Go2 archetype. Ironically, in the Go2 archetype, the Go-To player is not a post-player.
• Only three teams feature a top RbDs player that is not a post-player: 2003 SYR, 2014 CONN, and 2019 UVA. 2003 SYR's Carmelo Anthony was the prototypical Go-To player, so I wouldn't be worried about this one. 2014 CONN didn't have a post-player until the 5th spot in RbDs (a lot of this depends on how we classify 2014 CONN's DeAndre Daniels, which I felt was a struggle in both articles because of the various starting lineups of that team). 2019 UVA also didn't have a post-player until the 5th spot in RbDs, but this may have a lot to do with the pack-line defense.
• Only in the B3 archetype did post-play have no importance in FTDs, probably because it was all about the team's Big 3 scorers.
• Only two teams featured a 9-man rotation (according to the 10 MINs & X*0.4 parameters), whereas the rest were either 7- or 8-man rotation (although, you could potentially count 2001 DUKE and 2012 UK as 6-man rotations when basing it on the distribution of minutes played). When shots are distributed evenly, 9-man rotations would each get 11.1%, 8-mans would get 12.5, 7-mans would get 14.3%, 6-mans would get 16.7%, and 5-mans would get 20.0%. I found it interesting how close some of the actual cut-offs for ShDs mirrored these cutoffs for even distribution. Just look at each column in the ShDs in terms of how many are above and below these thresholds and the width of the margins on both sides of the cut-off points. It's why I stated that UK's 7-man rotation almost breaks the bounds of mathematics as six of their players were above the 13.8% level. In fact, their top five in shot distribution almost falls in line with the 6-man and 7-man even distributions. It is why I feel that I made a good choice in using ShDs as one of the factors in identifying a team's player rotation.
  

Larger Concerns for the Archetypal Approach:

1. This is a lot of detail for one component of one model for 6 wins in bracket prediction. In the 2022 Final Analysis, the NCP model looked at all of the components of 17 contenders. In some years, this count can be over 20 contending teams. To do this amount of work for this amount of teams in such a short time as BCW, it is akin to fitting a watermelon through the eye of needle.
2. The other pressing factor is the evolution of the game and its impact on the adaptability of the component (regardless if it is the Taxonomical Approach or the Archetypal Approach). As the game evolves with new (and sometimes stupid) rules and offensive/defensive philosophies, new archetypes will emerge. In 2010, this approach would have never identified DUKE as a national champion as the B3 archetype would not have existed at that time. The same would go for 2005 UNC and 2004 CONN. Likewise, at one point in the study, I identified eight different archetypes into which I could classify these 21 teams. In order to keep consistency with the Taxonomical Approach, I wanted to explain as much behavior as possible in as few labels as possible. Five archetype groups accomplishes what the eight did, so it seemed appropriate to go with this count (six taxonomies in the other article, even though it could have been more). To further this notion of malleability, I've stated that certain teams could be moved into another archetype and the metrical/statistical thresholds would only need to be altered slightly to differentiate between archetypes. This aspect alone gives more of a hindsight feel to the model even though it is being applied as tool for foresight.
   
3. In the Taxonomical Approach article, I raised about weighting in the model that I would like to address. If components of the NCP model counted as they currently existed, PG production, Taxonomical Approach, and Archetypal Approach would count as three weightings. In theory, the archetypal approach is the PG production combined with the taxonomical approach, sort of like fitting two puzzle pieces together. Should they count separately as three weights in the NCP model? Or should the two mirco-approaches (PG and taxonomical) be evaluated as validation/invalidation thresholds for the macro-approach (archetypal) and the whole puzzle count as one weight in the NCP model? In 2022, I kind of split the difference, counting PG as one weight and post-production (merging the two approaches into one measure) as another weight. In all honesty, I think only a full back-testing of the NCP model itself could answer which question is correct, so I'll stay with the same weightings as 2022 for now.

As always, thanks for reading my work, and like I said in Part 1, if you see any typos or any glaring misses, feel free to speak up in the comments section.