In our first two parts, we dug into the history books to determine whether or not the need to replace volume scoring had a negative impact on offensive efficiency going forward. While the general conclusion seems to be that other than at the extremes there is very little correlation between scoring lost and offensive efficiency, what fun would it be to throw up our hands and say "well, hopefully someone will score, let's be done with it!"
Now before you dig in too far, please keep the following in mind. This has been expanded to a five-part series, and while some of the parts may have less satisfying takeaways than readers (and, frankly, the author) expected, to fully understand the goal of the team and the overall final projections, you will need to read the series all the way to the end.
Photo by Charles Fox | Philadephia Inquirer
When projecting who will lead a team in scoring, there are really three main factors:
- Minutes Played: If you aren't on the court, you aren't going to score. However, because simply being on the court doesn't guarantee you will score, it can't be considered alone.
- Percent of Shots Taken: Usage rate alone doesn't tell the story because it factors in possessions that end in ways other than with a shot taken. Looking at the percentage of shots a player gets while on the floor indicates how many scoring chances they will have.
- Effective Field Goal Percentage: Stat nerds (and I imagine most readers here) have long preferred eFG% to other measures of shot efficiency. The easiest explanation is that it is field goal percentage with a 50% boost given to made three point shots because 3 is 50% more than 2.
To approximate next year's performance, we looked at players with similar past profiles to Marquette's returnees and weighed those past seasons against their future results. When you look at the charts, the portion above the break is that player's comparison player average (comps listed at the end of each player's synopsis) and the player's past and projected future numbers are below the break.
After breaking those down, we will be able to project 2022-23 expected minutes played, percent of shots taken, and effective field goal percentage. Those numbers will be balanced against 60 shots per game to produce an expected points per game average, with a slight adjustment for free throw rate and percentage using last year's numbers.
We will also provide expected best and worst case scenarios by using the three top and bottom comps from individual samples. For all players, we took the three biggest single-year comparable jumps and smallest comparable jumps (or biggest declines) and applied the average of those three to each player's 2021-22 season at Marquette. Let's count down the three most likely candidates to lead the team in scoring.
In his first year at Marquette, Kolek became far more of a creator than he was at George Mason as his percent shots and eFG% both dropped. While that seems like a negative harbinger for his scoring, it's worth noting that the comps we found of players moving up a level often went through similar situations. Looking at Kolek's comparable players, when you counted their pre-transfer seasons twice and their first season at the new program once, then averaged those three, it had a near perfect second-year post-transfer correlation. The respective percentages were accurate to 1.6% of minutes, 0.2% of percent shots, and 0.02% of eFG%. We used that transfer correlation for Kolek only (it didn't fare the same with O-Max's comps). Let's check the numbers:
| %Min | %Shot | eFG% | PPG | |
| Pre-Transfer | 76.8 | 25.0 | 50.3 | |
| Transfer Year 1 | 60.5 | 19.2 | 49.3 | |
| Transfer Year 2 | 73.5 | 23.0 | 50.0 | |
| Tyler Kolek (PT) | 75.5 | 19.5 | 53.1 | 10.8 |
| Tyler Kolek (TY1) | 72.3 | 16.7 | 40.0 | 6.7 |
| Tyler Kolek (22-23) | 74.4 | 18.6 | 48.7 | 9.3 |
| Tyler Kolek (Worst) | 76.6 | 18.2 | 38.4 | 7.7 |
| Tyler Kolek (Best) | 80.4 | 22.4 | 48.7 | 12.2 |
The first thing to note is that based on his comparable players, Kolek is highly likely to bounce back in terms of eFG%. Most of the comparable players that saw an eFG dip in their first post-transfer year improved that in their next year. With an expected increase in minutes and usage, Kolek will almost certainly increase his scoring as well. I was a little surprised by the 12.2 ppg best case comparison. That will likely depend on his role in 2022-23. If he continues to primarily play point guard and run the offense, that number is probably the best case scenario, while if he moves off the ball his percent of shots taken and eFG% could both be higher.
It's worth noting we have already seen improvement with Kolek in his first year. He shot just 22% from deep in non-conference games but improved that to 33.3% in Big East play. He also hit 51.4% of his catch-and-shoot threes, so if others are creating for Kolek rather than him creating for himself (he hit a dismal 14.7% on threes off the dribble) he could even exceed his best case scenario. In addition, the early reports out of camp is that Kolek looks like the most improved player on the team and has been scorching the nets after apparently taking the criticisms of his shooting last season personally. Expecting him to be around double-digit scoring is a relatively safe assumption.
Tyler Kolek Comparisons: Jared Bynum, Donald Carey, Torin Dorn, Elijah Harkless, Ithiel Horton, Koby McEwen, Quincy McKnight, Marcquise Reed, Andrew Rowsey, Eric Williams
Worst Case Comparisons: Ithiel Horton, Quincy McKnight, Eric Williams
Best Case Comparisons: Jared Bynum, Elijah Harkless, Koby McEwen
Already showing up on some of the 2023 NBA Draft boards, O-Max seems like the safest bet to make a jump in 2022-23. Our comparisons back that up, as even his worst-case options project to improve his percent of shots and eFG%. Quite simply, similar statistical players that played limited minutes in their first collegiate year, then transferred and saw big minute jumps like Prosper did in their second year tended to continue ascending in year three. There was no equivalent comparison like Kolek that incorporated the pre-transfer year, so we strictly did transfer year one to year two improvement percentages. With Justin Lewis gone, it seems likely Prosper will get every chance to increase his scoring.
| %Min | %Shot | eFG% | PPG | |
| Pre-Transfer | 21.8 | 16.3 | 45.2 | |
| Transfer Year 1 | 51.7 | 17.8 | 51.8 | |
| Transfer Year 2 | 60.2 | 20.2 | 56.4 | |
| O-Max Prosper (PT) | 22.3 | 17.3 | 37.5 | 2.5 |
| O-Max Prosper (TY1) | 51.2 | 16.8 | 51.8 | 6.6 |
| O-Max Prosper (22-23) | 59.5 | 19.1 | 56.4 | 9.4 |
| O-Max Prosper (Worst) | 47.1 | 19.5 | 53.7 | 7.3 |
| O-Max Prosper (Best) | 77.6 | 18.2 | 62.3 | 12.7 |
In all honesty, the best-case scenario might be the most realistic for Prosper, and it's possible he could even exceed that. 80% of the comparable players improved in percent of shots taken, 80% improved in eFG%, and 90% increased their minutes. Double-digit scoring seems highly likely as he will get all the shots he previously got and many that Justin would've taken last year. Though while the gut feel is that the best case scenario is attainable, it did seem a bit surprising that the second highest scoring projection was still under 10 ppg.
Olivier-Maxence Prosper Comparisons: Nick Babb, Jemarl Baker, Colin Castleton, Luke Fischer, Dan Fitzgerald, Anton Gill, Myke Henry, Tariq Owens, Jonathan Tchamwa Tchatchouwa, Jamil Wilson
Worst Case Comparisons: Jemarl Baker, Colin Castleton, Myke Henry
Best Case Comparisons: Nick Babb, Anton Gill, Tariq Owens
I imagine it's no surprise to see Kam Jones as the expected leader on this list. His three point shooting ability, high usage, and a long but productive comparable list really lead to high expectations. However, there are some rather unencouraging comparisons that keep us from ruling out a sophomore slump and while his best case scenario is lofty, it isn't the highest best case scenario we could see, but that will come in the next part. Let's break out the numbers:
| %Min | %Shot | eFG% | PPG | |
| Freshmen Sample | 43.8 | 20.7 | 48.7 | |
| Sophomore Sample | 64.1 | 21.9 | 49.8 | |
| Kam Jones (Fr) | 44.5 | 22.9 | 55.9 | 7.4 |
| Kam Jones (22-23) | 65.1 | 24.2 | 57.2 | 11.3 |
| Kam Jones (Worst) | 47.8 | 23.0 | 51.1 | 7.1 |
| Kam Jones (Best) | 84.6 | 25.9 | 68.9 | 18.9 |
The average improvements for Kam's comps come up favorable, but considering how far he was ahead of the average eFG%, it seems unlikely he will jump far (almost certainly not to the best case scenario). This admittedly includes some players who saw precipitous eFG% drops, such as Markus Howard and Steve Novak, who mostly fell because they were otherworldly in terms of shooting the ball as freshmen.
I would caution those excited by the big numbers in Jones' best case scenario, a guard reaching 68.9% eFG% is highly unlikely. Not impossible, but his three best case scenario comps all came from players who had freshman year eFG% in the 40s, so they had significantly more room to grow and it was easier for them to have large percentage jumps because of their initial low shooting percentages, unlike Jones for whom it will be harder to improve on 55.9%. I think Jones is the most likely player to lead the team in scoring and reach double-digits but he might also be the least likely to hit his best case scenario.
Kam Jones Player Comps: Brendan Bailey, Justin Blake, Vander Blue, Sandy Cohen, Eric Davis, Jase Febres, Lazar Hayward, Markus Howard, Ryan Kreklow, Justin Lewis, Dameon Mason, Wesley Matthews, Shemiye McLendon, Steve Novak, Kerwin Roach
Worst Case Scenarios: Justin Blake, Eric Davis, Shemiye McLendon
Best Case Scenarios: Brendan Bailey, Jase Febres, Justin Lewis
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