Welcome Guest! Create an Account login email: password: remember me: BASKETBALLFOOTBALLMESSAGE BOARDCHATLINKS site searchcontact usabout usadvertise with ushelp Message Board BobcatAttack.com Message Board Ohio Basketball Topic:  Transfer Portal - An Analysis of the Haul Topic:  Transfer Portal - An Analysis of the Haul Author Message QuantCat General User Member Since: 7/15/2025 Post Count: 57 Status: Offline     Transfer Portal - An Analysis of the Haul    Posted: 4/8/2026 12:25:41 PM  With portal season heating up, I wanted to take a look back at our past transfer hauls, where each player ranked coming in, and how they actually performed. My plan is to use this post to break down our past portal classes, and then come back once we have a roster in place to analyze the current class and what it means for the upcoming season. All data displayed is from EvanMiya.com. Why is EvanMiya the gold standard for college basketball analytics? Historically, sites like KenPom and Torvik leveraged advanced metrics to analyze teams on a per-possession basis. This helps normalize analysis for pace and puts fast and slow teams on an even playing field. EvanMiya builds on those tools by starting with line-by-line, play-by-play data. Because of this, his model generates metrics that account for pace and for who is actually on the floor. If a game is in junk time and a bench player scores a ton of points on the other team's benchwarmers, this system accounts for that directly and won't artificially prop the player up. What are the metrics? - OBPR: The impact a player has on the offensive side of the floor. An OBPR of 0 is the average across NCAA D1. A player like Jason Preston with a 4.81 is well above average, and someone like Jalen Breath with a -1.93 is below average. - DBPR: The impact a player has on the defensive side of the floor. Similar to OBPR, the D1 average is 0. Someone like Jason Carter with a 2.35 is an above-average defender, and someone like Dior Conners with a -1.78 is a below-average defender. - BPR: OBPR + DBPR. This is the player's overall impact on the game. Unsurprisingly, the highest of the Boals era was Jason Preston with a 6.37 (4.81 OBPR + 1.57 DBPR). Others that stick out include Mark Sears with a 5.44 (4.67 OBPR + 0.77 DBPR) and Jaylin Hunter with a 5.39 (4.37 OBPR + 1.02 DBPR). - Projected BPR: A projection of how good a transfer portal player is going to be the following season. This attempts to account for situations where a player may have been elevated or held back by their previous team being exceptionally good or bad. Let's walk through past portal classes, starting with 2022 and going through last year. (Note: Players who transferred up from non-D1 schools, like Tommy Schmock or Vic Searls, do not have data for projections). Past Portal Prospects (Year - Player: Previous Season BPR | Projected BPR | 1st Season Actual BPR) - 2022 - Jaylin Hunter: 2.36 | 2.56 | 5.49 - 2022 - Gabe Wiznitzer: -0.77 | 0.04 | -3.76 - 2022 - DeVon Baker: -0.06 | -0.59 | 0.37 - 2023 - Shereef Mitchell: -0.86 | -0.08 | 2.47 - 2023 - Ike Cornish: -2.15 | -0.65 | -2.29 - 2024 - Jackson Paveletzke: 0.89 | 0.58 | 1.15 - 2025 - Javan Simmons: 1.94 | 2.70 | 2.20 - 2025 - Jalen Breath: 0.16 | 0.25 | -1.39 - 2025 - Dior Conners: -0.90 | -0.51 | -2.82 Who were the biggest wins relative to their projections? - Jaylin Hunter: Came in as a very good portal prospect and emerged as one of the best players in the conference for two years. His defensive impact metrics really stick out - he was a good offensive player, but his defensive stats made him an elite MAC player. - Shereef Mitchell: Outperformed expectations. Worth noting that his first two seasons at Creighton saw numbers similar to what he did at Ohio, so his projections were likely skewed down by a single bad season. Who were the biggest losses relative to their projections? - The Wiz: Projected to be a solid rotational piece but netted out his first season as a -3.76, one of the worst rotational seasons in the Boals era. Fun fact: The Wiz was the first player that honed me in on the EvanMiya data. I had high hopes for a former 4-star, 6-11 center, but noticed early on that the other team tended to go on runs when he was on the court. The data very much supported that theory. - Conners and Breath: Both ranked as roughly average portal prospects, and both saw significant regressions from where they were in prior seasons. Moral of the story: players projected to be high-value prospects (Hunter/Simmons) tend to hold up and, in the case of Hunter, outperform projections. Players projected to be negatives tend to remain negatives, and average players tend to remain exactly that. Are the projections perfect? Absolutely not. The model needs enough historical data on a player to be accurate, and it really struggles to predict outcomes for guys who transfer down without much playing time. The model forces those players toward average, which makes them incredibly volatile - a Daniel Freitag at Buffalo can project as average and emerge as a top player in the conference, while an Ike Cornish can project as average and turn out to be a negative. Even with players who have a lot of minutes on tape, it is hard to account for system fit from one spot to another. I plan to follow up in this thread when the portal haul is complete to compare where the team projects out relative to past teams. As a sneak preview, Kyler D'Augustino projects as an even better portal prospect than Simmons. EvanMiya predicts him to be a 3.13 for next season, making him the highest-rated portal prospect we have landed in the portal era. It is also a huge plus that he is a local kid and will likely bring in some extra attendance. Back to Top M.D.W.S.T General User Member Since: 12/23/2021 Post Count: 3,711 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 4/9/2026 9:20:04 AM  What I love about KDA is this kid is as excited to be a Bobcat as I've ever seen. His whole comment section on instagram is about how he used to dream of playing at OU. Multiple times people were like "You always said you'd be here", "Remember when...?", "We talked about this when we were kids..." Happy as hell for the kid. And I think we got a good one. Not just as a person, but a ball player. Back to Top FJC31 General User Member Since: 3/31/2022 Post Count: 2,382 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 4/9/2026 9:38:20 AM  Great info, Quant. It's always been apparent one of our staff's strengths has been adding PGs via the portal. This shows that and I expect continuity with that in KDA. Simmons aside, it's everywhere else where seem to be lacking which has shown in the roster imbalance and on court results. Back to Top Bobcat1998 General User Member Since: 11/7/2012 Post Count: 2,772 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 4/9/2026 9:49:45 AM  QuantCat wrote: With portal season heating up, I wanted to take a look back at our past transfer hauls, where each player ranked coming in, and how they actually performed. My plan is to use this post to break down our past portal classes, and then come back once we have a roster in place to analyze the current class and what it means for the upcoming season. All data displayed is from EvanMiya.com. Why is EvanMiya the gold standard for college basketball analytics? Historically, sites like KenPom and Torvik leveraged advanced metrics to analyze teams on a per-possession basis. This helps normalize analysis for pace and puts fast and slow teams on an even playing field. EvanMiya builds on those tools by starting with line-by-line, play-by-play data. Because of this, his model generates metrics that account for pace and for who is actually on the floor. If a game is in junk time and a bench player scores a ton of points on the other team's benchwarmers, this system accounts for that directly and won't artificially prop the player up. What are the metrics? - OBPR: The impact a player has on the offensive side of the floor. An OBPR of 0 is the average across NCAA D1. A player like Jason Preston with a 4.81 is well above average, and someone like Jalen Breath with a -1.93 is below average. - DBPR: The impact a player has on the defensive side of the floor. Similar to OBPR, the D1 average is 0. Someone like Jason Carter with a 2.35 is an above-average defender, and someone like Dior Conners with a -1.78 is a below-average defender. - BPR: OBPR + DBPR. This is the player's overall impact on the game. Unsurprisingly, the highest of the Boals era was Jason Preston with a 6.37 (4.81 OBPR + 1.57 DBPR). Others that stick out include Mark Sears with a 5.44 (4.67 OBPR + 0.77 DBPR) and Jaylin Hunter with a 5.39 (4.37 OBPR + 1.02 DBPR). - Projected BPR: A projection of how good a transfer portal player is going to be the following season. This attempts to account for situations where a player may have been elevated or held back by their previous team being exceptionally good or bad. Let's walk through past portal classes, starting with 2022 and going through last year. (Note: Players who transferred up from non-D1 schools, like Tommy Schmock or Vic Searls, do not have data for projections). Past Portal Prospects (Year - Player: Previous Season BPR | Projected BPR | 1st Season Actual BPR) - 2022 - Jaylin Hunter: 2.36 | 2.56 | 5.49 - 2022 - Gabe Wiznitzer: -0.77 | 0.04 | -3.76 - 2022 - DeVon Baker: -0.06 | -0.59 | 0.37 - 2023 - Shereef Mitchell: -0.86 | -0.08 | 2.47 - 2023 - Ike Cornish: -2.15 | -0.65 | -2.29 - 2024 - Jackson Paveletzke: 0.89 | 0.58 | 1.15 - 2025 - Javan Simmons: 1.94 | 2.70 | 2.20 - 2025 - Jalen Breath: 0.16 | 0.25 | -1.39 - 2025 - Dior Conners: -0.90 | -0.51 | -2.82 Who were the biggest wins relative to their projections? - Jaylin Hunter: Came in as a very good portal prospect and emerged as one of the best players in the conference for two years. His defensive impact metrics really stick out - he was a good offensive player, but his defensive stats made him an elite MAC player. - Shereef Mitchell: Outperformed expectations. Worth noting that his first two seasons at Creighton saw numbers similar to what he did at Ohio, so his projections were likely skewed down by a single bad season. Who were the biggest losses relative to their projections? - The Wiz: Projected to be a solid rotational piece but netted out his first season as a -3.76, one of the worst rotational seasons in the Boals era. Fun fact: The Wiz was the first player that honed me in on the EvanMiya data. I had high hopes for a former 4-star, 6-11 center, but noticed early on that the other team tended to go on runs when he was on the court. The data very much supported that theory. - Conners and Breath: Both ranked as roughly average portal prospects, and both saw significant regressions from where they were in prior seasons. Moral of the story: players projected to be high-value prospects (Hunter/Simmons) tend to hold up and, in the case of Hunter, outperform projections. Players projected to be negatives tend to remain negatives, and average players tend to remain exactly that. Are the projections perfect? Absolutely not. The model needs enough historical data on a player to be accurate, and it really struggles to predict outcomes for guys who transfer down without much playing time. The model forces those players toward average, which makes them incredibly volatile - a Daniel Freitag at Buffalo can project as average and emerge as a top player in the conference, while an Ike Cornish can project as average and turn out to be a negative. Even with players who have a lot of minutes on tape, it is hard to account for system fit from one spot to another. I plan to follow up in this thread when the portal haul is complete to compare where the team projects out relative to past teams. As a sneak preview, Kyler D'Augustino projects as an even better portal prospect than Simmons. EvanMiya predicts him to be a 3.13 for next season, making him the highest-rated portal prospect we have landed in the portal era. It is also a huge plus that he is a local kid and will likely bring in some extra attendance. You are forgetting Sylvester Ogbonda from his first year, Dwight Wilson from Year 2 and Tommy Schmock from year 3 along with Vic Searls two years ago. All portal guys. I would contend that Vest was a great role player and that team was going to shock the MAC Tourney if COVID didn't happen. DW3 had a great career. And Schmock, in my opinion, was a baller. Vic was Vic. Nothing flashy but a solid piece. Back to Top Bobcat Tattoo General User Member Since: 12/11/2022 Post Count: 256 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 4/9/2026 10:03:09 AM  Bobcat1998 wrote: QuantCat wrote: With portal season heating up, I wanted to take a look back at our past transfer hauls, where each player ranked coming in, and how they actually performed. My plan is to use this post to break down our past portal classes, and then come back once we have a roster in place to analyze the current class and what it means for the upcoming season. All data displayed is from EvanMiya.com. Why is EvanMiya the gold standard for college basketball analytics? Historically, sites like KenPom and Torvik leveraged advanced metrics to analyze teams on a per-possession basis. This helps normalize analysis for pace and puts fast and slow teams on an even playing field. EvanMiya builds on those tools by starting with line-by-line, play-by-play data. Because of this, his model generates metrics that account for pace and for who is actually on the floor. If a game is in junk time and a bench player scores a ton of points on the other team's benchwarmers, this system accounts for that directly and won't artificially prop the player up. What are the metrics? - OBPR: The impact a player has on the offensive side of the floor. An OBPR of 0 is the average across NCAA D1. A player like Jason Preston with a 4.81 is well above average, and someone like Jalen Breath with a -1.93 is below average. - DBPR: The impact a player has on the defensive side of the floor. Similar to OBPR, the D1 average is 0. Someone like Jason Carter with a 2.35 is an above-average defender, and someone like Dior Conners with a -1.78 is a below-average defender. - BPR: OBPR + DBPR. This is the player's overall impact on the game. Unsurprisingly, the highest of the Boals era was Jason Preston with a 6.37 (4.81 OBPR + 1.57 DBPR). Others that stick out include Mark Sears with a 5.44 (4.67 OBPR + 0.77 DBPR) and Jaylin Hunter with a 5.39 (4.37 OBPR + 1.02 DBPR). - Projected BPR: A projection of how good a transfer portal player is going to be the following season. This attempts to account for situations where a player may have been elevated or held back by their previous team being exceptionally good or bad. Let's walk through past portal classes, starting with 2022 and going through last year. (Note: Players who transferred up from non-D1 schools, like Tommy Schmock or Vic Searls, do not have data for projections). Past Portal Prospects (Year - Player: Previous Season BPR | Projected BPR | 1st Season Actual BPR) - 2022 - Jaylin Hunter: 2.36 | 2.56 | 5.49 - 2022 - Gabe Wiznitzer: -0.77 | 0.04 | -3.76 - 2022 - DeVon Baker: -0.06 | -0.59 | 0.37 - 2023 - Shereef Mitchell: -0.86 | -0.08 | 2.47 - 2023 - Ike Cornish: -2.15 | -0.65 | -2.29 - 2024 - Jackson Paveletzke: 0.89 | 0.58 | 1.15 - 2025 - Javan Simmons: 1.94 | 2.70 | 2.20 - 2025 - Jalen Breath: 0.16 | 0.25 | -1.39 - 2025 - Dior Conners: -0.90 | -0.51 | -2.82 Who were the biggest wins relative to their projections? - Jaylin Hunter: Came in as a very good portal prospect and emerged as one of the best players in the conference for two years. His defensive impact metrics really stick out - he was a good offensive player, but his defensive stats made him an elite MAC player. - Shereef Mitchell: Outperformed expectations. Worth noting that his first two seasons at Creighton saw numbers similar to what he did at Ohio, so his projections were likely skewed down by a single bad season. Who were the biggest losses relative to their projections? - The Wiz: Projected to be a solid rotational piece but netted out his first season as a -3.76, one of the worst rotational seasons in the Boals era. Fun fact: The Wiz was the first player that honed me in on the EvanMiya data. I had high hopes for a former 4-star, 6-11 center, but noticed early on that the other team tended to go on runs when he was on the court. The data very much supported that theory. - Conners and Breath: Both ranked as roughly average portal prospects, and both saw significant regressions from where they were in prior seasons. Moral of the story: players projected to be high-value prospects (Hunter/Simmons) tend to hold up and, in the case of Hunter, outperform projections. Players projected to be negatives tend to remain negatives, and average players tend to remain exactly that. Are the projections perfect? Absolutely not. The model needs enough historical data on a player to be accurate, and it really struggles to predict outcomes for guys who transfer down without much playing time. The model forces those players toward average, which makes them incredibly volatile - a Daniel Freitag at Buffalo can project as average and emerge as a top player in the conference, while an Ike Cornish can project as average and turn out to be a negative. Even with players who have a lot of minutes on tape, it is hard to account for system fit from one spot to another. I plan to follow up in this thread when the portal haul is complete to compare where the team projects out relative to past teams. As a sneak preview, Kyler D'Augustino projects as an even better portal prospect than Simmons. EvanMiya predicts him to be a 3.13 for next season, making him the highest-rated portal prospect we have landed in the portal era. It is also a huge plus that he is a local kid and will likely bring in some extra attendance. You are forgetting Sylvester Ogbonda from his first year, Dwight Wilson from Year 2 and Tommy Schmock from year 3 along with Vic Searls two years ago. All portal guys. I would contend that Vest was a great role player and that team was going to shock the MAC Tourney if COVID didn't happen. DW3 had a great career. And Schmock, in my opinion, was a baller. Vic was Vic. Nothing flashy but a solid piece. From QuantCat's post: "Note: Players who transferred up from non-D1 schools, like Tommy Schmock or Vic Searls, do not have data for projections." As for Vest and Dwight, EvanMiya is pretty new, so he doesn't have data going back that far. But if I had to guess, I'd wager Dwight was a strong positive, beyond what was expected, and Vest was in the 0.5-1 range. Back to Top QuantCat General User Member Since: 7/15/2025 Post Count: 57 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 4/9/2026 10:41:45 AM  Bobcat Tattoo wrote: Bobcat1998 wrote: You are forgetting Sylvester Ogbonda from his first year, Dwight Wilson from Year 2 and Tommy Schmock from year 3 along with Vic Searls two years ago. All portal guys. I would contend that Vest was a great role player and that team was going to shock the MAC Tourney if COVID didn't happen. DW3 had a great career. And Schmock, in my opinion, was a baller. Vic was Vic. Nothing flashy but a solid piece. From QuantCat's post: "Note: Players who transferred up from non-D1 schools, like Tommy Schmock or Vic Searls, do not have data for projections." As for Vest and Dwight, EvanMiya is pretty new, so he doesn't have data going back that far. But if I had to guess, I'd wager Dwight was a strong positive, beyond what was expected, and Vest was in the 0.5-1 range. It’s exactly this, a limitation of the EvanMiya data is that it only does analysis on division 1 players and games. Completely agree with you on Vic and Tommy, Tommy might be one of my favorite bobcats I’ve watched in the convo. Not the most talented player we’ve had but seemed to just be everywhere on the court, hit a lot of big shots, and really embraced the role he was given. The other limitation is how far back his projection data for transfers goes. I started with 2022 because that was the first year he had projections for everyone. The overall data goes back to 2009-2010, it’s just that the specific functionality of projecting portal guys for how well the would play the next year is newer. I’ll include the D1 transfers he got before that data is available by listing out their ratings from the year before they came to Ohio and then their first year. Pre 2022 D1 portal grabs (year - player: prior season rating | first season actual rating) 2021 - Jason Carter: 2.76 | 3.36 2020 - Dwight Wilson: -0.42 | 2.26 2019 - Sylvester Ogbonda: 0.27 | 0.91 Carter is obviously a unique situation since he boomeranged and already had relationships with guys on the team. Regardless, getting him back was a win, as his ratings over the years were incredibly consistent between here and Xavier. He was never rated lower than 2.5 and peaked at around a 3.5 in the last Saul year. Last Edited: 4/9/2026 10:43:03 AM by QuantCat Back to Top QuantCat General User Member Since: 7/15/2025 Post Count: 57 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/1/2026 12:06:18 AM  Part 1 - The How Now that we're closer to a full roster, I wanted to follow up on the earlier post, and am breaking this up into 4 parts: Part 1: A quick overview of what I did and how Part 2: An overview of OU Part 3: An overview of MAC Part 4: A final wrap-up My original plan was simple: look at who each MAC team brought in through the portal, compare it to who they kept, and figure out what it all meant for the Bobcats. I figured it would come together quickly like it has in years past. Then the staff went international, things slowed down, and I suddenly had time on my hands. So, with that time and a little boredom, I started pulling in a ton of data: - All EvanMiya (EM - https://evanmiya.com /) player and team data back to 2016-17 - All 247 and On3 recruiting data back to 2018 - RealGM profiles and stats for all 16,000 players who touched NCAA Division 1 basketball from 2019 to today. The nice thing about RealGM is that it carries a player's entire basketball history: international play before and after college, D2, D3, JUCO, NAIA, and national team play (think U19 World Cup). One quick note before going further. The number I keep referencing is EM's BPR. The simplest way to think about it is a single number for how much a player (or team) helps win games. A 0 is roughly a replacement-level Division 1 player, positive is good, negative is below that line, and the further from 0, the more extreme. I used all of that data to build models that predict a BPR for every player and every team, every season. Players get sorted into categories, and each category has its own model built specifically for it: - Freshmen: uses 247/On3 recruiting data plus a 3-year rolling average of the team's strength. Unranked guys are graded off the strength of the team they're going to (an unranked commit to Akron is probably better than one to CMU). Elite prospects like Cooper Flagg get an extra bump if they're projected as lottery picks before the season. - Returners: looks at a player's trajectory (steady climbers vs. guys who hit the same number every year) and how many years they've played. Returning redshirts are modeled off prior experience if it exists, or dropped to a lower team baseline if they're a Carter Reese type. - D1-to-D1 transfers: starts with the EM projection, then moves up or down based on the destination team and other factors. - Non-D1 transfers: measures a player's strength relative to other non-D1 players, factors in the team they're heading to, and projects them off historical data. - International recruits: there have been fewer than 250 international players (with no U.S. high school experience) in the NCAA over the last 7-plus years. There have, however, been a huge number of players go the other way, from the NCAA to international leagues. So my approach was to build a "dominance score" for a player within his league, find former D1 players who dominated that same league at a similar level, and comp his projection against how those guys did in college. Every player and team then gets three numbers: a Floor, a Base, and a Ceiling. By design, that range only tries to capture the middle 50% of outcomes. Across the last 7 years of data, the actual result across NCAA D1 teams land inside the predicted range about 50% of the time, with 25% overperforming and 25% underperforming. That is on purpose. It's impossible to predict things like injuries, or a team that suddenly gels (or one that never does). Back to Top QuantCat General User Member Since: 7/15/2025 Post Count: 57 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/1/2026 12:09:16 AM  Part 2 - The OU Analysis With one roster spot left to fill, Ohio currently projects at a 3.3, nearly identical in talent to the 2024-25 group that was picked to win the MAC before injuries derailed the season. One important note: the preseason projection is only a measure of the talent on the roster. When a team truly gels, there's usually a lift across the board and they blow past expectations. On the other end, teams that battle injuries (2024-25) or simply never fit together (2025-26) drag everyone down and underperform their talent. For reference, here are the past projections from my model next to what actually happened: - 2020-21: 1.8 projected -> 8.2 actual (Mark Sears' breakout freshman year, plus Preston and BVP pulling the whole team up) - 2021-22: 9.1 projected -> 4.6 actual (Roderick regression, Carter injuries, lack of depth) - 2022-23: -1.0 projected -> 2.9 actual - 2023-24: 5.0 projected -> 4.0 actual - 2024-25: 3.3 projected -> -0.9 actual (Reef, Clayton, Brown, and Hadaway all hampered by injuries) - 2025-26: -0.2 projected -> -4.3 actual (key transfers underperformed) - 2026-27: 3.3 projected -> ?? actual Team preseason projections are built like this: 1. Classify each player (D1 transfer, returner, freshman, international, etc.) 2. Run each player individually through the model built for his category 3. Estimate a share of minutes for each player. No player can take more than 20% of a team's minutes. A guy like Pav, who played big minutes and rated near the top of the team, lands around 17-18%. A redshirt freshman projected lower lands around 3-5%. This generally only goes about 10 players deep, and assumes the bottom 5 get little to no playing time. 4. Add it up: (Player 1 projection x minutes share) + (Player 2 projection x minutes share) + (Player 3 projection x minutes share), and so on. So how does that 3.3 come together? Here is every player with a quick blurb, in order of projection: 1. Kyler D'Augustino (3.4 - D1 transfer): Kyler was a 0.5 at IU Indy last season, but the EM transfer model flagged him as exactly the type of player likely to take a big jump when surrounded by better offensive talent. Even on a bad team, he posted incredibly efficient 53/39/79 shooting splits and finished extremely well at the rim. My transfer model starts with the EM projection (3.1) and adjusts up or down for team quality among other metrics. 2. Javan Simmons (2.9 - returner): In his first three years, Javan has gone 1.5, 1.9, and 2.2 - steady, year-over-year improvement. My personal take is that the model actually undervalues him. It's anchored to last year's 2.2, and that 2.2 is dragged down by a first half of the season where he struggled to find his footing. If he picks up where he left off in February and keeps improving, 2.9 could end up light. In my mind he should be the number 1 guy on this team, but the model has to be applied consistently with the data available. 3. Kolton Mitchell (2.4 - D1 transfer): Over three seasons, Mitchell has gone -2.1, 0.8, and 2.0. He's a shot creator who took a high volume of shots on a tournament team at Idaho, with 40/36/80 splits. Not as efficient as KDA, but he can get his own shot while averaging close to 4 assists vs. 1.6 turnovers. He in my mind should be the starting PG. 4. Siebe Ledegen (2.1 - international): The BNXT is a strong league, and Ledegen was a very good starter in it. There was a large pool of players with similar production in that league who came from strong NCAA programs and posted EM numbers as high as 5 or 6 (Preston/Sears/BVP level). To be clear about how the comp works: because Siebe averaged 13/4/2 on 51/41/82 splits, he's compared to players who made a similar impact, not to 20-point-per-game scorers and not to bench warmers. 5. Dusan Makitan (1.2 - international): The BiH Liga MVP rates lower than Siebe because the BiH Liga is a much weaker league than the BNXT. Of the international leagues that have sent players to the NCAA between 2019 and 2026, the BiH Liga ranks 44th/46, while the BNXT ranks a respectable 14th/46. Dusan was an MVP, but against weaker competition, so he projects at a solid 1.2. I think Siebe is a lock to be solid, while Makitan comes with a much wider range of outcomes. I could see him busting to a -2, and I could just as easily see him dominating as a 4-plus player. For reference, his countryman and fellow BiH Liga standout Harun Zrno projected at 1.8 last year for Rutgers but finished at -0.3. 6. Che Brogan (-0.5 - international): The only data RealGM had for him came from national team play at the U19 World Cup and similar events. The data is a bit of a black hole, but Brogan has clear strengths and weaknesses that also line up with his NBL1 data from outside RealGM. He consistently posts good assist and steal numbers with average-to-below-average shooting. In the NBL1 this past season he went (per 36 minutes) 20.5 points, 7.3 rebounds, 8.6 assists, and 2.5 steals on 41/33/70 splits. His U19 World Cup production also showed strong defense and playmaking, and his comp pool for that event landed him at -0.5. He'll have a role as a true point guard with size who can defend, with upside if the shooting and finishing come along. 7. Moek Icke (-0.7 - D1 transfer): No playing time at Rhode Island in two seasons, so there's very little to go on beyond the EM portal projection. He'll have a very wide range of outcomes and could be anywhere from a -4 to a 2 if he settles in. 8. Zay Mosely (-0.8 - redshirt): Redshirt freshmen get a baseline from the 3-year rolling average of the team's overall strength. He'll have a big range of outcomes and could make an already strong roster even better if he has a redshirt breakout like Javan did at Toledo. 9. Jordie Bowens (-1.1 - unranked freshman): Unranked freshmen also get a default baseline based on the 3-year rolling team strength. These guys are very volatile - they can turn into a JJ Kelly (above 0) or fall completely out of the rotation. 10. Jesse Burris (-1.2 - returner): Burris was a -2.3 last year, but OU has a history of players making big jumps from their first to second year of real playing time (I count both Burris and Kuany as entering their second year). Hadaway (+4.5), Preston (+3.2), Clayton (+3.4), Roderick (+2.8), and Brown (+2.6) all saw huge year-over-year growth from Year 1 to Year 2. Burris is a breakout candidate, but I think he lands closer to that -1.2, and if anyone in this group breaks out, I'd bet on Kuany. 11. Kiir Kuany (-1.3 - returner): Kuany posted a very low -3.6 last year, but he's a prime candidate for a Hadaway/Clayton type of Year 1 to Year 2 jump. If he can pair a better feel for the game with his size, shooting stroke, finishing, and on-ball defense, he could climb well above that -1.3. He has as wide a range of outcomes as anyone on the roster: he could repeat the -3.6, settle in around -1.3, or break out to a 2-plus. 12. Carter Reese (-1.5 - returner): Non-D1 transfers who got no playing time in Year 1, and guys who have gone two straight years without real minutes (Ben Estis), get dropped to a baseline for what the model treats as potential walk-ons. 13. Christian Trudic - I had this write up ready before he was spotted with the team. Given the lack of info, it is safe to assume he will be a redshirt and wouldn't impact the team projection. Back to Top QuantCat General User Member Since: 7/15/2025 Post Count: 57 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/1/2026 12:09:57 AM  Part 3 - The MAC If we have a solid group of returners, D1 transfers, and international guys, where does that leave us against the rest of the MAC when the same models are applied across the board? To be clear, this isn't me manually typing in numbers I like. These are automated models grounded in 16,000-plus players across many seasons, and each player type (international vs. returner vs. the rest) has its own model fit specifically to it. That said, I did use my own knowledge of the MAC and Ohio to sanity-check each model and tweak where I saw problems. For example, the early returner model badly underrated last year's Miami team, projecting guys like Eian Elmer to get worse year-over-year. There was no world where a player with Elmer's clear track record of improvement should be projected to decline, so that became a validation point. Every model went through multiple rounds of checks and iterations to make sure it landed somewhere sensible. Now, the state of the MAC. Using the same models on a fully automated basis, here is how it ranks out. Teams with an asterisk still have roster spots to fill. 1. Miami (3.9 - 9 players above 0): Miami lost a ton of talent (Byers, Suder, Woolfolk, Elmer) but kept key pieces and added some strong transfers. Their biggest strength is far and away the best guard trio in the league: Luke Skaljak (projected MAC Player of the Year), transfer Stevie Elam (4th in the MAC), and Evan Ipsaro (7th in the MAC). The question mark is what Steele gets out of his bigs and wings. Almar Atlason, Rich Rolf, and Preston Copeland project at 1.1, 0.9, and 0.6. Can Almar break out? Can Rolf build on his outlier 1.4 season at Youngstown? Can Copeland take a Year 2 jump on a better team? Miami is the clubhouse leader heading into the season, and you can probably bank on that big trio outperforming. 2. *Ohio (3.3 - 5 players above 0): The individual projections have Ohio holding 3 of the top 11 players in the league entering the season. This is a roster with very good talent at the top. The potential weaknesses are depth relative to WMU/Akron/Miami/Kent (there's a hard drop-off past the top 5) and the volatility that comes with international recruits. We'll lean heavily on those international guys hitting, and the data shows that while the model is serviceable there, those players can be very hit or miss. 3. Kent State (1.6 - 8 players above 0): Kent brings back Quinn Woidke (projected 2nd in the MAC), who in almost any other year, without Leroy Blyden, would have been the league's Freshman of the Year. They also get Jamal Sumlin, a 2.3 player in 2024-25, back from a season lost to injury. Around them is a strong group of transfers plus a highly touted freshman in Dhani Miller, who has as good a shot as any MAC freshman at a Blyden/Sears-type breakout. Like Miami, Kent will have excellent guard depth and will likely play three guards at all times. What makes or breaks their season, and could make them a title winner, is their less-proven group of bigs. 4. *Akron (0.6 - 10 players above 0): Akron lost both a coach who always seemed to have his teams overperforming, and a significant amount of talent. The model nailed their dominant season last year, projecting them as the 75th-best team in the country (9.0); they finished 76th (9.6). They brought in a mix of proven mid-major players (Staveskie, Pence, Hammer, Burton) plus two high-upside guys in Colin White (Ohio State) and Exavier Wilson (Kansas State). In two seasons with real minutes at Ohio State, White posted a 1.5 and a 0.0. For comparison, Evan Mahaffey went 0.6, 1.5, and 3.9 over three years at Ohio State before jumping to a 5.4 at Akron. White has real breakout potential in a bigger role. This team's ceiling won't be what it was in 2025-26, but Akron will be a contender, like they always are. 5. UMass (-0.8 - 6 players above 0): UMass pairs two key returners, Jayden Ndjigue (2.7 in 2025-26) and Danny Carbuccia (0.7 as a freshman and a prime breakout candidate), with three key D1 transfers in Jordan Clayton (Northwestern), Junjie Wang (San Francisco), and Abdullah Ahmed (BYU), plus a key international addition in Noa Zemljic. They are massive, with six players 6'9" or taller alongside a good guard tandem and a wing in Jayden. If the new group gels and they get some freshman contributions, they could be a threat. The downside is a lack of depth, and top-end players that project a bit lower than the other teams at the top of the MAC. 6. Western Michigan (-1.1 - 8 players above 0): This is the breakout candidate from the bottom of the league. Over the last six years, WMU has always projected somewhere between -7.7 and -12.8, and has never actually finished better than the -9.3 they posted last season. This year they absolutely crushed the transfer portal at every position and added a solid international center in Janis Nemann. Here's the big caveat: several of the models, particularly the transfer and freshman models, use each team's 3-year rolling average as a predictive input. If you replace WMU's rolling average of around -10 with a 0, this team jumps all the way up to right behind Ohio in the rankings. This is not the WMU of the past. They're going to be a real threat. 7. Toledo (-2.2 - 6 players above 0): The Rockets bring back three core returners, led by Austin Parks (1.5 in 2025-26, projected for 2.3 in 2026-27) along with Will James (1.7 projected) and Mynor Strong (1.5 projected). This is a team that lost its three best players (Blyden, Craig, Wilson) and replaced them with transfers in Sean Jones, Rich Barron, and Angel Montas. Those transfers all have 3-plus years of EM data suggesting they'll land as average MAC starters and not much more. The wild card is their four freshmen. Todd K has a real track record of finding talent other teams overlook, and while it's unlikely, another Blyden-type could once again lift this team into contention. 8. Bowling Green (-4.2 - 7 players above 0): BG has a solid group of about eight players projected between -0.3 and 1.7, made up of two returners, four D1 transfers, and two non-D1 transfers (Todd Simon has a strong track record of finding lower-division talent). This is a team with real depth that should protect them from bottoming out, but becoming a contender would require multiple breakouts or the team playing well above the sum of its parts. They've been feisty the last few years and should remain a tough out. 9. Ball State (-5.2 - 6 players above 0): This is a team with only three returners and a new staff that completely rewired the roster. Their seven D1 transfers include two high-major "transfer downs" with very wide ranges of outcomes, plus several proven, solid mid-major players. Looking through the data, high-major transfer downs (Aaron Fine and Jazz Henderson) are very unpredictable: most land around their new team's average, a lot bust (like Cornish), and a select few have Daniel Freitag-type seasons. This team will be competitive, but in my mind not a true contender at the top. 10. Buffalo (-6.8 - 3 players above 0): Another team with very few returners, only one of whom played real minutes last year (Kyle Jones, 280 total possessions). They have seven D1 transfers, two non-D1 transfers, one freshman, and four "returners," only one of whom has touched the floor. Their portal haul is interesting: they landed a true stud and All-MAC contender in Drew Steffe out of Southern Illinois. The other six transfers have real playing experience, but only one of them has ever rated above a 0 by EM. This could look a lot like last year's Buffalo team, one dominant player who can win games with an unproven supporting cast. 11. *Eastern Michigan (-7.6 - 3 players above 0): I'll start by saying Billy Donlon was a phenomenal hire for EMU. This is a coach who was fired after a 22-win season at Wright State (where he had 19-plus wins in four of six seasons, three of them 21-plus) and again after 19 wins at UMKC in 2021-22, before spending the last few years on Brad Brownell's staff at Clemson (the same guy who hired him as an assistant at WSU). He'll make the most of limited resources at EMU. He's already retained MAC All-Freshman honoree Mohammad Habhab along with Greg Lawson and Braelon Green. Beyond the returners, they have three slightly unproven D1 transfers and six non-D1 transfers. With two roster spots still to fill, they project as a team that can win some games under a good coach, but whose ceiling is probably sneaking into the MAC tournament. 12. Central Michigan (-9.1 - 3 players above 0): This team will be led by returners Phat Phat Brooks and Keenan Garner. Beyond those two, it's a whole host of new faces: one D1 transfer, three non-D1 transfers, and four unranked freshmen. That comes after last season, when their coach (who came from a D1 program) brought in nine transfers from the lower ranks, meaning in two years they've added 12 non-D1 transfers (look away, Fishbates). It's tough to see a path where this team contends, let alone makes the MAC tournament. Back to Top QuantCat General User Member Since: 7/15/2025 Post Count: 57 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/1/2026 12:12:08 AM  Part 4 - Closing Thoughts All of this data is available here https://019f1bc7-750d-ab96-90e4-8725089bfbd0.share.connec... / in an interactive dashboard you can click through. It has six main areas: - Team Rankings: see how teams project versus how they actually finished each season, filterable by conference. Click any team's row and a second view appears below, showing how individual players made up that team's projection and performance. - Player Rankings: see how individual players project versus how they finished, filterable by conference, team, and possessions played. There's also a "Show" toggle that controls how many players appear, so set it to All to see everyone. Like the team table, clicking a player generates a view below. Click Javan Simmons, for example, and you'll get a graph and table of his season-by-season performance against his projections. - Team History: look at an individual team's results versus projections over time, with every player and season in one table. - International - Players: this shows every player since 2019 who arrived in the NCAA with an "international" tag. It works like the Player Rankings (click a player to visualize his year-by-year performance) with two additions: you can filter by international league, and it shows each player's per-36 stats from the year before college. Filter for BiH Liga, for instance, and you can see exactly who else came from that league and how they did. - International - League Strength: the top table shows the backward (NCAA-to-league) and forward (league-to-NCAA) comparisons. Backward comps measure a league's strength by the average peak NCAA BPR of players who went there; forward comps focus on the first year for players who came here. Click a league to generate two tables underneath showing both pools. - Methodology: a deeper dive into how it all works. Note: on Team Rankings and Player Rankings, 2026-27 currently only projects MAC teams. Once RealGM updates rosters closer to the season, I'll run a full update projecting every player and team for 2026-27, so we can see how Ohio's 3.3 stacks up nationally and against non-conference opponents. Addressing the model's weaknesses: - International: there are fewer than 250 "international" players (meaning no U.S. high school experience) who also played > 400 possessions in their first year. To model them, I reference former D1 players from their respective leagues. This still carries real volatility in both directions. Some international guys, for whatever reason, just never find their footing in the U.S. - Stud freshmen: recruiting ratings and rankings from sites like 247/On3 are terrible predictors of freshman year performance. So, I tried to balance being accurate with being justifiable. Before I brought in preseason draft boards to separate the true studs (Cooper Flagg) from a player ranked 10th in his class, the model would peg Flagg at a 5. That's statistically "accurate" in the sense that it fits to the middle, but it wasn't justifiable. Even with these corrections, the model will never correctly predict MAC breakout freshmen like Blyden and Sears, because plenty of other highly rated MAC recruits busted. - Coaches who consistently develop players: if you want to have some fun, go look at those Akron rosters and how Groce consistently took guys who started at -2 or -1 and made them better year after year until they were MAC-POY-caliber. I could try to build this into the model, but a lot of these coaches don't stay in one place very long, and you'd end up overfitting by chasing it. State of the Bobcats - What the coaching staff did this offseason was genuinely impressive. The coaching staff said they were resetting the program after multiple years of continuity and they brought in a group consisting of guys from 5 different countries. - Depth is the potential downfall of this particular group. The Bobcats project 2nd in the conference because of a very strong starting five, but they have far less depth on paper than the other contenders. Right now, no OU player outside the starting five projects at 0 or above. Every other team in the top 9 has at least one, and the other clear contenders have at least three. The good news is the upside is there: if three of the Kuany/Brogan/Burris/Mosely/Icke/Bowens group net out above 0, this team could quickly climb to the top of the league. So the goal is essentially a 50% hit rate on that group, and my personal picks to get it done would be Kuany, Brogan, and Icke. - Does the highest rating mean the team automatically wins the MAC? Absolutely not. In the six years of data, a preseason favorite has won the MAC tournament exactly once (Akron last year). This is purely a measure of talent brought in, and it's very hard, near impossible, to predict injuries and team fit. On the fit point alone: you can assemble eight guys who complement each other perfectly on paper, but if those eight guys can't stand each other, it's tough to play good basketball. - Not Bobcat-related, but worth repeating: keep an eye on Western Michigan. They're a real threat to the entire MAC now. Back to Top FJC31 General User Member Since: 3/31/2022 Post Count: 2,382 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/1/2026 9:44:43 AM  Nicely done, QuantCat. Great stuff here. Any analysis on Chansey Willis at Kent? To me, he's the biggest incoming transfer given his already body of work in the MAC and now he's with Senderoff. After digesting this more, it seems pretty important that our staff finds use of at least 1/2 remaining schollies. Especially on the guard front. I echo the thoughts that Che, Kuany, and Moek end up being the more impactful bench players this season. How impactful will be the story. I think everyone on this board is excited about the potential of starting five including Javan, Dusan, Siebe, Kolten, and KDA. Western Michigan -- yea, this team will be a problem. Excluding Junemann and other additions (#189 overall player and #23 PG Jordan Sigmon), WMU's D1 transfers alone combined for 15.8 win shares this past season. Only 2 out of the 8 players started at least 30 games. 6'8 Augustinas Kiudulas might be due for a nice bounce back after going from a starter at VMI (15ppg, 3.2 WS) to a role player at Colorado State. 3 others in this portal class are 6'6 and including Kiudulas, all hit the 3 at least 39%. This is going to be a very tough team. Back to Top GoCats105 General User Member Since: 1/31/2006 Location: Seattle, WA Post Count: 7,837 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/1/2026 10:26:15 AM  FJC31 wrote: After digesting this more, it seems pretty important that our staff finds use of at least 1/2 remaining schollies. Especially on the guard front. I echo the thoughts that Che, Kuany, and Moek end up being the more impactful bench players this season. How impactful will be the story. I think everyone on this board is excited about the potential of starting five including Javan, Dusan, Siebe, Kolten, and KDA. Yeah the depth is the biggest worry for me. If any of those five starters go down and some roster shuffling needs to be done, this season could go South in a hurry. Fingers crossed Ohio can avoid the injury bug that's seemingly plagued this program for about a decade now. Back to Top QuantCat General User Member Since: 7/15/2025 Post Count: 57 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/2/2026 10:14:27 AM  FJC31 wrote: Any analysis on Chansey Willis at Kent? To me, he's the biggest incoming transfer given his already body of work in the MAC and now he's with Senderoff. Willis is an interesting one, as he is very likely under predicted. His first 2 seasons were 1.8 (2024-25 WMU) and 0.9 (2025-26 Minnesota). The EM projection for him in the transfer portal data was a 1.1 which got weighted down by his regression at Minnesota. My projection doe transfers uses the EM portal number as a starting point and adjusts up or down. Mine saw the peak of 1.8 and a team destination of Kent and bumped him up to 1.3. This Kent team has even more depth at guard than Miami. Miami has the 3 headed monster, but Kent may very well go 5 deep with All-MAC potential quality guards. Kent always plays more of a defensive minded game so interested to see if Senderoff goes full small ball. Back to Top Bobcat Love's Sense of Shame General User Member Since: 7/30/2010 Post Count: 4,696 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/2/2026 12:04:37 PM  Nothing to add, but thanks for this analysis, QuantCat. Very interesting stuff. This portal haul definitely looked good, so glad to see some metrics that bear that out. Back to Top GraffZ06 General User Member Since: 1/5/2005 Location: Dayton, OH Post Count: 2,445 Status: Offline     RE: Transfer Portal - An Analysis of the Haul    Posted: 7/2/2026 7:26:03 PM  Great data and analysis Quant. I live for this stuff. Back to Top Showing Replies:   1 - 15  of 15 Posts Jump to Page:   1 View Other 'Ohio Basketball' Topics                                                                                                                                                                                                                                                                               WHO'S ONLINE There are 246 Active Visitors on BobcatAttack.com 246 Guests 0 Registered Users There are no other users looking at this page.

Copyright ©2026 BobcatAttack.com. All rights reserved.  |  Privacy Policy  |  Terms of Use
Partner of USA TODAY Sports Digital Properties