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No, you’re not.
tbh queen should 7-35 from 3 for the season and CMB shot 9-34
im not hopefu about either. though i think queen is a more skilled handler/passer than CMB and seems more comfortable hitting pull up middies. queen also shot about 77% from the line compared to 71% for CMB
with that said, if you are looking at compatability at the 4, queen's lateral movement defensively is... ok-ish. CMB is very good in that respect.
i dont like either for us personally. but queen has more upside for sure
No, you’re not.
Spurs set up their own 60 player draft board,and draft the highest remaining guy when one of their picks rolls around. They don’t draft based on concensus or mocks. They do have different requirements from most teams. I’ve always said that probably only 20-25% of NBA players could play here.
It does seem to be true that the Spurs will take players off their board if they have character or culture fit concerns.
Which makes the Joshua Primo pick that much more hilarious in hindsight
Everybody misses, sometimes. All you can do is a background check, and if people hide from you, there isn’t a lot you can do.
1. The Spurs got burned as by Kawhi, who turned out to be a turncoat asshole of the highest order.
2. Primo did show signs of being a good dude, and I'm sure Nick Oats gave glowing recs when he was called, among other people.
2b. Whether Nick Oats knew about his sexual obsession of exposing himself and lied about it, we don't know.
3. The Spurs ed up by not taking Sengun, as many here wanted. So did a lot of teams. Presti drafted him and immediately trade him to the rival Rockets.
4. It will never be clear whether Primo would have been alright if he could have been developed. He was showing flashes, although was a long-term thing.
But yeah, character became an even bigger consideration after Kawhi stabbed an icy dagger in their back.
Analytics Deep Dive 11
DRAPM, short for Defensive Regularized Adjusted Plus-Minus, is a basketball statistic that measures a player's defensive impact relative to the team's performance on a per-100 possessions basis, after accounting for the quality of their teammates and opponents. It's part of the broader RAPM (Regularized Adjusted Plus-Minus) system, which also includes ORAPM (Offensive RAPM). RAPM is considered a more unbiased and accurate method of evaluating player performance compared to traditional box score statistics
Here's a more detailed breakdown:
1. What is RAPM?
- RAPM is an advanced statistic that attempts to isolate a player's impact on a team's scoring margin, controlling for the strength of their teammates and opponents.
- It's calculated using play-by-play data from multiple seasons, typically the last three, and uses a linear regression model to estimate a player's contribution.
- RAPM is considered a more robust measure of player impact than traditional plus-minus because it accounts for the quality of the players on the court at any given time.
2. How is DRAPM calculated?
- DRAPM is a component of RAPM, specifically measuring a player's defensive contribution.
- It's calculated using the same principles as RAPM, but focusing on the defensive impact.
- The calculation involves analyzing each possession, considering the players on the court, their roles (offensive or defensive), and the resulting score differential.
3. Why is DRAPM important?
- DRAPM helps to quantify a player's defensive impact beyond what's visible in traditional box scores.
- It provides a more objective assessment of a player's defensive capabilities, especially in the context of their specific team and matchups.
- It can be used to identify defensive strengths and weaknesses, as well as to compare players on a more equitable basis.
4. How to interpret DRAPM?
- A higher DRAPM value generally indicates a greater defensive impact.
- DRAPM is typically expressed in points per 100 possessions, allowing for easier comparison between players.
- By comparing a player's DRAPM to the league average or to other players, you can gain insight into their defensive effectiveness.
In essence, DRAPM is a powerful tool for basketball analysts and fans who want to go beyond traditional box score statistics to understand a player's true impact on the defensive end of the court
Here are the top DRAPM players in this draft class (excluding international players)
- Thomas Sorber- 6.1
- Kam Jones- 5.0
- Cooper Flagg- 5.0
- Sion James- 4.4
- Walter Clayton Jr.- 4.3
- Derik Queen- 4.1
- Danny Wolf- 4.0
- Kon Knueppel- 3.9
- Carter Bryant- 3.6
- Jase Richardson- 3.4
- Nique Clifford- 3.4
- Liam Mcneely- 3.2
- Tyrese Proctor- 3.2
- Adou Thiero- 3.2
- Chaz Lanier- 3.1
- Kobe Johnson- 3.1
- Collin Murray-Boyles- 3.1
- Boogie Fland- 2.8
- Khaman Maluach- 2.7
- Miles Byrd 2.5
Top 20 DRAPM prospects
Some other curious grades:
Dylan Harper has a negative score with -1.1. 5th worst out of every prospect in this class
Maxime Raynaud also had a negative score with -0.8
Yaxel Lendeborg scored a lot lower than I thought he would with a 0.6
Analytics deep dive 12
In basketball, ORAPM (Offensive RAPM) is a advanced stat that quantifies a player's offensive impact, specifically how many more points their team would score per 100 possessions with them on the court compared to an average replacement player. It's a component of RAPM (Regularized Adjusted Plus-Minus),a widely used metric that also includes DRAPM (Defensive RAPM) to assess overall player value.
Here's a more detailed explanation:
- RAPM:
RAPM is a statistical model that aims to isolate the individual impact of a player on their team's performance, regardless of their teammates and opponents. It does this by using data on every player's presence on the court during every stint (period of time) and then modeling the impact of that presence.
- ORAPM and DRAPM:
ORAPM and DRAPM are two parts of RAPM that break down a player's overall impact into offensive and defensive contributions. A positive ORAPM means the player is contributing positively to their team's scoring, while a positive DRAPM means they are contributing positively to their team's defense.
- How ORAPM works:
ORAPM is calculated by using a statistical regression model that analyzes a player's performance in various aspects of offense, such as scoring, assisting, and rebounding. The model then uses this data to predict how many more points per 100 possessions a team would score with the player on the court.
- Interpreting ORAPM:
A +1 in ORAPM means that the average lineup would score 1 more point per 100 possessions with that player added. Therefore, a player with a high ORAPM is considered to be a strong offensive contributor.
- Importance of ORAPM:
ORAPM is a valuable tool for basketball analysis, as it provides a more nuanced understanding of a player's impact than traditional statistics like points per game. It can help teams identify players who are strong offensive contributors, even if they don't have high scoring numbers.
- Example:
If a player has an ORAPM of +5.0, it means their team would score 5 more points per 100 possessions with them on the court than with an average replacement player
Top ORAPM players in this draft class
- Johni Broome- 6.5
- Kon Knueppel- 5.4
- Mark Sears- 5.0
- Walter Clayton Jr.- 4.9
- Kam Jones- 4.8
- Tyrese Proctor- 4.7
- Sion James- 4.7
- Yaxel Lendeborg- 4.5
- Jaxson Robinson- 4.4
- Darrion Williams- 4.4
- Koby Brea- 4.4
- Khaman Maluach- 4.3
- Will Riley- 4.3
- Jeremiah Fears- 4.3
- Isaiah Evans- 4.3
- Nique Clifford- 4.2
- Eric Dixon- 4.1
- Jase Richardson- 4.0
- Derik Queen- 3.9
- Carter Bryant- 3.8
Top 20 prospects in ORAPM
Some other curious grades:
Maxime Raynaud also scored super low in this category with 0.4
Thomas Sorber scored a 0.6
Analytics deep dive 13
RAPM, or Regularized Adjusted Plus-Minus, is a basketball analytics metric that assesses a player's impact on their team's scoring margin, while accounting for the strength of their teammates and opponents. It is considered an unbiased and impartial measure that captures both offensive and defensive contributions. RAPM uses a technique called regularization (ridge regression) to reduce standard errors in the calculation of Adjusted Plus-Minus (APM)
Regularized Adjusted Plus/Minus (RAPM) is undoubtedly the most influential one-number metric in basketball analytics today, boosted by its unbiased nature (relatively speaking) which is impartial to playstyle and craftily captures both offense and defense on a fair playing field.
Here are the top RAPM players in this draft class
- Kam Jones- 9.8
- Walter Clayton Jr- 9.5
- Kon Knueppel- 9.2
- Sion James- 9.1
- Cooper Flagg- 8.6
- Derik Queen- 8.0
- Tyrese Proctor- 7.9
- Johni Broome- 7.7
- Nique Clifford- 7.6
- Danny Wolf- 7.6
- Jase Richardson- 7.4
- Carter Bryant- 7.4
- Mark Sears- 7.1
- Khaman Maluach- 7.0
- Thomas Sorber- 6.7
- Will Riley- 6.2
- Adou Thiero- 6.2
- Ace Bailey- 6.0
- Labaron Philon- 5.9
- Darrion Williams- 5.9
Top 20 in RAPM
Some other curious grades:
Trevon Brazile had the worst score in this draft class at -1.0 overall
Maxime Raynaud is really hated with this analysis with a score of -0.4
Dylan Harper grades out as the 15th worst prospect with a 1.5
Analytics deep dive 14
In basketball, Defensive Win Shares (DWS) measures a player's contribution to their team's defense, estimating the number of wins a player contributes through their defensive play. It's essentially a gauge of how many wins are added to a team's record due to a player's defensive efforts. DWS is calculated by considering a player's marginal defense and dividing it by the marginal points per win.
Here's a more detailed breakdown:
- Marginal Defense:
This refers to how much a player's presence on the court improves their team's defensive rating compared to when they are off the court.
- Marginal Points per Win:
This is the average number of points a team needs to score to win a game, calculated based on the team's performance.
- Calculation:
DWS is calculated by dividing the player's marginal defense by the marginal points per win. This essentially translates the player's defensive impact into a win-value.
Key Considerations:
- Tempo-Free:
DWS is a tempo-free stat, meaning it doesn't rely on the pace of the game, providing a more consistent measure of defensive impact.
- Scalable:
One DWS is equivalent to one win contributed to the team.
- Limitations:
While DWS is a useful metric, it's important to remember that it's just one piece of the puzzle when evaluating a player's defensive abilities. It doesn't capture all aspects of defense, such as non-statistically visible defensive actions like positioning and timing.
- Luck Factor:
Like many defensive stats, DWS can be influenced by luck, as a player might play great defense but still have shots fall in.
In essence, Defensive Win Shares provide a valuable, tempo-free estimate of a player's defensive impact on their team's win-loss record, offering a more comprehensive perspective than basic defensive statistics alone
Top Defensive Win Share players in this draft class
- Cooper Flagg- 3.4
- Nique Clifford- 3.0
- Derik Queen- 3.0
- Rasheer Fleming- 2.8
- Johni Broome- 2.7
- Danny Wolf- 2.7
- Kon Knueppel- 2.5
- Ryan Kalkbrenner- 2.5
- Sion James- 2.3
- Walter Clayton Jr- 2.3
- Kobe Johnson- 2.3
- Miles Byrd- 2.3
- Tyrese Proctor- 2.2
- Yaxel Lendeborg- 2.2
- Maxime Raynaud- 2.2
- Chaz Lanier- 2.2
- Khaman Maluach- 2.1
- Coleman Hawkins- 2.1
- Hunter Sallis- 2.1
- Kam Jones- 2.0
- Darrion Williams- 1.9
- Collin Murray-Boyles- 1.9
- Jase Richardson- 1.8
- Thomas Sorber- 1.7
- Vj Edgecombe- 1.7
Top 25 in Defensive win shares
Analytics Deep Dive 15
Offensive Win Shares (OWS) in basketball is a statistic that estimates how much a player's offensive contributions have helped their team win games. It's part of the larger Win Shares metric, which attempts to divide the credit for team success among individual players. OWS specifically focuses on a player's impact on the offensive side of the court, considering factors like scoring, passing, usage rate, and efficiency.
Here's a more detailed breakdown:
- Purpose:
OWS provides a measure of a player's offensive value, quantifying how their contributions have led to additional wins for their team.
- Calculation:
The calculation of OWS is complex and involves several steps, including:
- Estimating the number of points a player produces.
- Calculating the number of offensive possessions they use.
- Determining the marginal offense they provide, which is the difference between the points produced and what would be expected based on league average and the number of possessions.
- Calculating marginal points per win based on team pace and league pace.
- Finally, dividing the marginal offense by the marginal points per win to arrive at the Offensive Win Shares.
- Factors Considered:
OWS takes into account various aspects of a player's offensive game, including:
- Scoring efficiency (points per possession).
- The number of possessions the player uses.
- The player's ability to create opportunities for themselves and others.
- The overall pace of the game and the league.
- Interpretation:
A higher OWS value indicates a player who has a greater impact on their team's offensive production and, consequently, their ability to win games.
In essence, OWS is a valuable tool for assessing a player's offensive contribution to team success, going beyond traditional statistics to provide a more nuanced understanding of their value
Here are the players with the most offensive win shares in this draft class
- Cooper Flagg- 5.0
- Ryan Kalkbrenner- 4.6
- Yaxel Lendeborg- 4.6
- Eric Dixon- 4.5
- Kon Knueppel- 4.4
- Walter Clayton Jr.- 4.3
- Johni Broome- 4.1
- Nique Clifford- 3.9
- Kam Jones- 3.8
- Asa Newell- 3.7
- Mark Sears- 3.6
- Khaman Maluach- 3.3
- Alex Karaban- 3.2
- Chaz Lanier- 3.0
- Jase Richardson- 3.0
- Collin Murray-Boyles- 3.0
- Rasheer Fleming- 3.0
- Derik Queen- 2.9
- Tre Johnson- 2.9
- Dylan Harper- 2.9
- VJ Edgecombe- 2.8
- Maxime Raynaud- 2.8
- Sion James- 2.8
- Darrion Williams- 2.8
- Tyrese Proctor- 2.8
Top 25 prospects Offensive Win Shares
Analytics Deep Dive 16
Win Shares
The final step of the process is to add Offensive Win Shares to Defensive Win Shares.
Here are the top prospects when in comes to win shares in this draft class
- Cooper Flagg- 8.4
- Ryan Kalkbrenner- 7.1
- Nique Clifford- 7.0
- Kon Knueppel- 6.9
- Yaxel Lendeborg- 6.8
- Johni Broome- 6.8
- Walter Clayton Jr- 6.6
- Derik Queen- 5.9
- Eric Dixon- 5.8
- Kam Jones- 4.8
- Rasheer Fleming- 5.7
- Khaman Maluach- 5.4
- Asa Newell- 5.2
- Chaz Lanier- 5.2
- Sion James- 5.1
- Tyrese Proctor- 5.0
- Maxime Raynaud- 5.0
- Collin Murray-Boyles- 4.9
- Jase Richardson- 4.8
- Alex Karaban- 4.7
- Darrion Williams- 4.7
- Mark Sears- 4.6
- Vj Edgecombe- 4.5
- Tre Johnson- 4.2
- Danny Wolf- 4.1
Analytics Deep Dive 17
The Value Point System (VPS) is an advanced basketball statistic that provides a comprehensive measure of a player's overall performance, considering both positive and negative contributions. It's calculated by taking a player's points, rebounds, assists, steals, blocks, and other factors, then dividing that by their missed shots, turnovers, and fouls. A higher VPS indicates a more effective player, with 1 being considered average and anything above 2 being considered elite.
Here's a more detailed breakdown:
- What it measures:
VPS aims to provide a holistic view of a player's impact, going beyond just counting stats like points.
- How it's calculated:
The formula involves a numerator that considers positive contributions (points, rebounds, assists, etc.) and a denominator that accounts for negative contributions (missed shots, turnovers, fouls).
- Interpretation:
A VPS of 1 is considered average, while a higher VPS indicates a more efficient and impactful player.
- Why it's valuable:
VPS can help identify players who might be undervalued by traditional stats, highlighting their efficiency and overall contribution to the team
Here are the 15 players that score as Elite in VPS in this draft class
- Ryan Kalkbrenner- 3.02
- Khaman Maluach- 2.54
- Yaxel Lendeborg- 2.40
- Cedric Coward- 2.38
- Collin Murray-Boyles- 2.24
- Cooper Flagg- 2.23
- Thomas Sorber- 2.17
- Johni Broome- 2.15
- Derik Queen- 2.09
- Adou Thiero- 2.05
- Nique Clifford- 2.04
- Sion James- 2.03
- Rasheer Fleming- 2.02
- Asa Newell- 2.00
- Jase Richardson- 2.00
A quick note. Every prospect in this draft that I could find graded at a least average so no worries there. These are the only Elite prospects in this analysis though.
Analytics deep dive 18
In basketball, "Warp" or "Wins Above Replacement Player (WARP)" is a metric used to evaluate a player's performance relative to a replacement-level player. It compares a team with a player and four average players to a team with four average players and a replacement-level player. The WARP system helps determine how many wins a player adds to a team compared to a player who can simply replace a spot on the roster.
Here's a more detailed explanation:
- Concept:
The WARP system evaluates a player by comparing the performance of a team with that player and four average players against a team with four average players and a replacement-level player.
- Calculation:
The method draws heavily on the work of Dean Oliver and aims to quantify the player's contribution in terms of extra wins they provide to the team.
- Importance:
WARP is a valuable tool for analyzing player performance, draft prospects, and understanding the impact of players on their team.
In essence, WARP helps to measure how many wins a player contributes above the level of a replacement-level player, providing a more nuanced understanding of player value than simple box score stats alone
Top Warp Scores in this Draft Class
- Yaxel Lendeborg- 12.1
- Cooper Flagg- 11.9
- Johni Broome- 11.7
- Nique Clifford- 11.2
- Ryan Kalkbrenner- 10.9
- Maxime Raynaud- 9.4
- Rasheer Fleming- 9.2
- Collin Murray-Boyles- 8.7
- Kam Jones- 8.3
- Eric Dixon- 8.2
- Walter Clayton Jr- 8.1
- Derik Queen- 8.1
- Asa Newell- 7.9
- Kon Knueppel- 7.8
- VJ Edgecombe- 7.1
- Danny Wolf- 6.9
- Khaman Maluach- 6.7
- Miles Byrd- 6.6
- Dylan Harper- 6.4
- Xavian Lee- 6.4
- Darrion Williams- 6.1
- Thomas Sorber- 6.0
- Alex Karaban- 5.8
- Adou Thiero- 5.5
- Chaz Lanier- 5.3
Top 25 prospects in WARP
Analytics Deep Dive 19
Ball Control and Usage Analysis for this Draft Class
I’m not going to go to deep into this one but I wanted to show who some of the better players are from this draft class at passing, controlling pace, ball control, taking care of the ball and usage.
1st I want to look at AST%
In basketball, AST% (Assist Percentage) is a statistic that estimates the percentage of a team's field goals a player assisted while they were on the floor. It essentially measures how often a player provides an assist during their time on the court.
How it's calculated:
While the exact formula can vary slightly depending on the source, the general idea is:
- Divide a player's assists by the number of their teammates' field goals while they were on the floor .
- This result is then multiplied by 100 to express it as a percentage.
What it tells you:
- AST% helps assess a player's passing ability and how well they distribute the ball to their teammates.
- A higher AST% generally indicates that a player is more involved in assisting on field goals when they are on the court.
- It's important to note that AST% alone doesn't tell the whole story. A player with a high AST% might also have a high usage rate (the percentage of possessions they use), which means they are more likely to have the ball in their hands and thus, more opportunities to get assists.
- To account for usage rate, the "assist-to-usage ratio" (Ast:Usg) is sometimes used, calculated by dividing AST% by the player's usage rate
Top 10 AST% players from this draft class
- Kam Jones- 38.1
- Xavian Lee- 37.3
- Egor Denim- 35.1
- Boogie Fland- 28.6
- Jeremiah Fears- 28.6
- Dylan Harper- 26.9
- Nique Clifford- 26.8
- Cooper Flagg- 26.5
- Mark Sears- 26.4
- Kasparas Jakucionis- 26.0
Next I want to look at AST Ratio
Assist ratio in basketball is a stat that shows how often a player or team helps another player score by passing the ball. It’s calculated by dividing the number of assists by the total number of possessions, then multiplying by 100. This number helps coaches and fans see how well a team shares the ball and works together to make good shots. A high assist ratio means the team is passing and creating opportunities, while a low ratio shows more individual play.
The idea of tracking assists has been around for a long time, but the assist ratiostat is a more modern way of measuring it. Early basketball stats mostly focused on points, but as the game evolved, analysts started looking for better ways to measure teamwork and ball movement. The assist stat became a key way to see how well a player could set up their teammates for success.
John Hollinger, a well-known basketball analyst, helped popularize the term “assist ratio” in the 2000s. He created it as part of his advanced basketball stats to better evaluate a player’s overall impact. Since then, assist ratio has been used by coaches, teams, and fans to measure how well players create scoring chances for others.
Formula – How to calculate Assist Ratio
Assist Ratio = (AST ÷ (FGA + (0.44 x FTA) + AST + TO)) x 100%
Where:
- “AST” are assists.
- “FGA” are field goal attempts.
- “FTA” are free throw attempts.
- “TO” are turnovers.
Top 10 AST Ratio players in this Draft Class
- Egor Denim- 35.1
- Sion James- 27.2
- Kobe Johnson- 26.1
- Boogie Fland- 25.8
- Labaron Philon- 25.1
- Xavian Lee- 23.7
- Coleman Hawkins- 23.5
- Kam Jones- 23.5
- Kasparas Jakucionis- 22.5
- Mark Sears- 21.4
Next I want to look at TOV%
In basketball, TOV% (Turnover Percentage) is a statistic that measures how often a team or player loses possession of the ball due to a turnover. It's calculated by dividing the number of turnovers by the sum of field goal attempts, free throw attempts multiplied by 0.44, and the number of turnovers.
Here's a more detailed breakdown:
- What it measures:
TOV% represents the percentage of possessions that end in a turnover, providing an indication of a player or team's ball-handling and decision-making efficiency.
- Formula:
TOV% = (Turnovers) / (Field Goal Attempts + 0.44 * Free Throw Attempts + Turnovers).
- Importance:
A lower TOV% is generally desirable, as it suggests better ball control and fewer wasted possessions.
- Usage:
TOV% can be used to evaluate the performance of individual players or entire teams, helping to identify players or strategies that are prone to turnovers.
Top Tov% players from this draft class
- Koby Brea- 4.9
- Chaz Lanier- 6.5
- Jaxson Robinson- 7.4
- Isaiah Evans- 7.5
- Jase Richardson- 8.0
- Asa Newell- 8.3
- Johni Broome- 8.6
- Tyrese Proctor- 9.1
- Tre Johnson- 9.3
- Will Riley- 9.3
Next I want to look at AST/TOV
This is the amount of Assists per turnover
Top 10 AST/TOV players from this draft class
- Boogie Fland- 3.72
- Kam Jones- 3.17
- Koby Brea- 2.82
- Xavian Lee- 2.52
- Sion James- 2.45
- Jase Richardson- 2.27
- Labaron Philon- 2.19
- Alex Karaban- 2.14
- Tyrese Proctor- 2.13
- Kon Knueppel- 2.02
The last stat I want to look at is USG%
In basketball, USG%, or Usage Percentage, estimates the percentage of a team's possessions a player uses while on the floor. It's a key advanced stat that helps analyze a player's role in the offense, the balance (or lack thereof) of a team's offense, and can be used in defensive strategy.
Here's a more detailed explanation:
- What it measures: USG% indicates how often a player is involved in their team's offensive plays. It's calculated by considering a player's field goals attempted, free throws attempted, turnovers, and then relating that to the team's total possessions when the player is on the court.
- Formula: The formula for calculating USG% is:
Code
USG% = 100 * (FGA + 0.44 * FTA + TOV) * (Team Minutes Played / 5) / (Minutes Played * (Team FGA + 0.44 * Team FTA + Team TOV))
Where:
- FGA = Field Goals Attempted by the player
- FTA = Free Throws Attempted by the player
- TOV = Turnovers by the player
- Team Minutes Played = The total minutes played by the team
- Minutes Played = The player's total minutes played
- Team FGA = Total Field Goals Attempted by the team
- Team FTA = Total Free Throws Attempted by the team
- Team TOV = Total Turnovers by the team.
- Interpretation: A player with a high USG% (e.g., 30% or higher) is likely a primary offensive option, meaning they take a large share of their team's shots and plays. A low USG% (e.g., under 20%) might indicate the player is a more supporting role, with a focus on other aspects of the game.
- Team context: A team with a well-balanced offense might have USG%s closer to each other, while a team with a star player might have a few players with much higher USG%s than others
- Defensive implications: USG% can help identify a team's key offensive players, which can inform defensive strategies
Top 10 USG% players in this draft
- Eric Dixon- 34.7
- Maxime Raynaud- 31.5
- Jeremiah Fears- 31.4
- Cooper Flagg- 30.8
- Johni Broome- 30.3
- Tre Johnson- 29.3
- Kam Jones- 29.0
- Dylan Harper- 28.9
- Chaz Lanier- 28.9
- Xaivian Lee- 28.5
Analytics Deep Dive 20
In basketball, FTA refers to Free Throws Attempted, which is the total number of free throws a player or team has taken during a game. FTA Rate, or Free Throw Rate, is a stat that compares a team's free throw attempts to their field goal attempts. The formula for Free Throw Rate is FTA / FGA (Field Goals Attempted). It essentially shows how often a team is at the free throw line relative to their overall shooting attempts.
Here's a more detailed breakdown:
- FGA (Field Goals Attempted):
This is the total number of field goal attempts a player or team makes during a game.
- FTA / FGA:
This ratio, the Free Throw Rate, provides a percentage of how often a team is at the free throw line compared to the total number of field goal attempts.
- Why it's important:
A higher FTA Rate can indicate a team's ability to draw fouls, which can lead to more scoring opportunities at the free throw line. It can also reflect a team's shooting style, as a team that favors drives to the basket may have a higher FTA Rate
Top FTA Rate Players in this draft class
- Adou Thiero- 69.2
- Collin Murray-Boyles- 55.6
- Derik Queen- 54.6
- Jeremiah Fears- 51.8
- Cedric Coward- 50.8
- Kasparas Jakucionis- 49.6
- Mark Sears- 47.8
- Sion James- 46.1
- Yaxel Lendeborg- 44.9
- Cooper Flagg- 42.0
- Thomas Sorber- 42.0
- Dylan Harper- 41.9
- Jase Richardson- 41.8
- Liam Mcneely- 41.0
- Khaman Maluach- 40.3
These are the top 15 players at getting to the line in this draft class
This is the last analysis I’ll post for now. Let me know if y’all like looking through these and I’ll keep posting them. A lot more data out there and I enjoy looking at it all.
I think we are all sleeping on Noa Essengue. (I did as well)
it's likely because the international class isn't that intriguing this year and so the focus is on the college players.
but I assume teams and scouts still do their homework in Europe and so I will try to point at a few things they currently register:
(note: the stats on tankathon just show his numbers in the Eurocup compe ion from September last year to January)
Noa has been playing quite well lately in the German league. over the last ten games he averaged 12.4 PPG on very good shooting splits and on increased volume. (57%FG, 38% 3P on 11-29, 70%FT) plus 5.1 RPG in 24 MPG.
so, to put things into perspective: the youngest player in the draft seems to develop nicely, especially in the departments of concerns. his team (currently 2nd in the league) will make the PO, so his season will likely go on till end of May.
btw. Noa meassured great for a combo forward in the Baskeball Without Borders combine. (6'9" w/o shoes, 6'11" wingspan, 9'3" standing reach. 35.5" vertical).
so, I assume he is much higher on the draft boards of the teams, than on the mocks.
yes, he ist still raw and still very thin, but team will consider that he is a year younger than many of the freshmen are and project his development.
so, is he a candidate for pick 8? maybe.
for pick 15? you bet.
Noa has a big ass head he should fill out more.
9’3” standing teach is huge that’s bigger than a lot of 7 footers.
Light rebounding, poor 3 point shooting. Most here are not interested in another shooting project.
ah yes. sorry that I didn't ask for your permission to post some points about a player. didn't know the rules.
That's probably enough copy/paste from Google AI for one thread. Thanks.
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