Green Grove Project

2017/18 Season Analysis Company Overview

We help the young athletes to accelerate their sport-specific growth through the statistical analysis.

Our services are based on a Decision Management Tool which integrates and analyses the Athletic and Bio-Athletic data creating valuable insights for the athletes and the teams. ______

International Award: Top-15 Rising Sports Analytics Startups in Europe (KPMG, 04/2018)

Presenters: • National Statistical Conference (Lamia – GR, 05/2018) • World Series Sports Analytics Conference (Amsterdam - NL, 05/2018)

Our website: statathlon.com Our website: statathlon.com Email Address: [email protected] Email Address: [email protected] Introduction Green Grove Project focuses on the individual and team performance of a club, using various data analysis methods. Insights of this project can be used by the clubs and all relevant stakeholders (coaching and medical staff, players etc.) in order to track its performance in different categories.

The case study used for this project is the Lithuanian club BC Zalgiris . The selection was based on the following criteria: • A highly reputed and ambitious professional club from Europe. • The club would participate in a major continental competition with adequate number of matches to be led to fair conclusions. In addition, a few non - playing criteria were set such as team location (more specifically, coming from a country with high interest in basketball) and strong business profile.

The data has been extracted from Euroleague Basketball Regular Season (2017-2018), where all 16 teams faced each other twice in 15 home and 15 away games.

01 AIMS & OBJECTIVES

Aims and Objectives

• To understand better the impact that various statistical • To help coaching staff identify the exact roles that are indexes have and find the correlation between them. missing from the current squad and look for players with similar skills in transfer market. • To assess each player’s performance in order to find his strengths and weaknesses, as well as his overall progress • To find out how certain players perform in games against throughout season. teams with different characteristics, in order to adjust defensive and offensive tactics accordingly in the future. • To provide useful recommendations to coaching staff regarding team’s actual strengths and weaknesses. Analysis • To explore team’s performance during specific phases of a outcomes can be used to determine if there are any fields game (early minutes, clutch time etc.). These results help the where team should pay more attention in training or during coaching staff to manage players more efficiently in relevant pre - game preparation. situations.

02 Green Grove Project Indexes Interpretation For the purposes of this project, more than 30 performance indexes have been used. They have split into 4 main categories, according to their complexity (basic & advanced) and content (individual & team).

Basic individual and team performance indexes are being used by Euroleague Basketball and everyone can have free access to them through its website or other partners’ websites. Advanced indexes are a mixture of indexes used in NBA, indexes that Euroleague Basketball does not directly provide and therefore their calculation is made through the official available data, and unique performance indexes which have been created by the research team of Statathlon.

In the following pages all advanced indexes are explained. Their interpretation includes specific comments on what could be considered as a good performance in each of them, as well as a few conclusions and recommendations that can be extracted out of them.

03 5 Current Squad of Zalgiris Kaunas

NAME POSITION HEIGHT (cm) AGE NATIONALITY JOINED C 208 26 USA 2017 Kevin Pangos PG 188 24 CAN/SVN 2016 Axel Toupane SF/SG 201 25 FRA 2017 Beno Udrih PG/SG 189 35 SVN 2017 Paulius Jankunas PF/C 205 33 LTU 2010 Martynas Sajus C 208 21 LTU 2017 Gytis Masiulis PF 206 19 LTU 2014 Arturas Milaknis SG/SF 195 31 LTU 2016 Vasilije Micic SG/PG 195 23 SRB 2017 Martynas Arlauskas SG 199 17 LTU 2017 Aaron White PF 206 25 USA 2017 C 208 33 LTU 2016 Paulius Valinskas PG/SG 191 22 LTU 2016 SF/PF 198 25 LTU 2014 Dee Bost PG 188 28 USA 2017

04 Green Grove Project 2017/18 Zalgiris Kaunas Fixtures

Game Date Opponent Home/Away Game Date Opponent Home/Away 1 13/10/2017 Crvena Zvezda Belgrade Home 16 5/1/2018 Crvena Zvezda Belgrade Away 2 20/10/2017 Khimki Moscow Region Away 17 12/1/2018 Unicaja Malaga Home 3 24/10/2017 FC Away 18 16/1/2018 Brose Baskets Away 4 26/10/2017 Real Madrid Home 19 18/1/2018 Valencia Basket Home 5 3/11/2017 CSKA Moscow Away 20 25/1/2018 Panhinaikos Away

6 9/11/2017 AX Armani Exchange Olimpia Milan Away 21 1/2/2018 Maccabi Tel Aviv Home

7 14/11/2017 Unicaja Malaga Away 22 9/2/2018 Khimki Moscow Region Home 8 16/11/2017 Baskonia Vitoria Gasteiz Home 23 23/2/2018 Anadolu Efes Istanbul Away 9 23/11/2017 Anadolu Efes Istanbul Home 24 1/3/2018 FC Barcelona Home 10 30/11/2017 Maccabi Tel Aviv Away 25 9/3/2018 Fenerbahce Istanbul Home 11 7/12/2017 Panathinaikos Athens Home 26 15/3/2018 Baskonia Vitoria Gasteiz Away 12 14/12/2017 Fenerbahce Istanbul Away 27 20/3/2018 AX Armani Exchange Olimpia Milan Home 13 19/12/2017 Brose Bamberg Home 28 22/3/2018 Real Madrid Away 14 21/12/2017 Valencia Basket Away 29 30/3/2018 CSKA Moscow Home 15 28/12/2017 Olympiacos Piraeus Home 30 6/4/2018 Olympiacos Piraeus Away

05 Green Grove Project Individual Performance

06 Green Grove Project 16 8 Individual Performance - GSI

Game Score Index (GSI)– Kevin Pangos & Paulius Jankunas appear to be the most ‘productive’ players during the year. 7 players perform better than team’s season average (4.4). Game Score Index presents the overall performance of an athlete. It is more accurate than Euroleague’s PIR index, since it emphasizes more to categories that have bigger impact on the game.

07 Green Grove Project Individual Performance – Assists & Turnovers

Assist Percentage - 3 players perform better than team’s index Turnover Percentage - Vasilije Micic has very poor (25.33). Despite his position, Edgaras Ulanovas shows high performance on ball handling, by committing approximately 1 assisting ability. turnover every 4 attempts. An effective index that evaluates playmaking and passing skills of a Usually higher in guards than forwards or centers. Players with high player. Players with good performance in this category are usually turnover percentage should not be heavily involved in actions that those who should run the offence. have to do with ball handling as they are quite prone to turning the ball over.

08 Green Grove Project Individual Performance – Blocks & Steals

Block Percentage – Despite his position, Beno Udrih shows Percentage – 4 players perform better than team’s high blocking ability. In addition, Ulanovas has the highest season average (1.69). Despite his position, Brandon Davies blocking percentage despite his height (1,98m) and position. appears to be the most effective ‘stealer’. Good indicator of defensive (and particularly blocking) skills of a player. Players with high steal percentage values are considered good Quite useful especially for power forwards or centers, who are the defenders and could be a good suggestion to guard opponent point ones that are usually more likely to a shot. More than 2 guards for instance or be on floor when teams want to pressure. blocks/game is considered good value.

09 Green Grove Project Individual Performance – Free throws & Rebounds

Free Throw Rate (%) – Despite his impact on Zalgiris’ game, Rate (%) – Arturas Milaknis & Vasilije Micic are the Kevin Pangos has the 2nd worse rate among his teammates. least effective players on the rebounding sector Moreover, Paulius Jankunas has the 5th best Rate and FT% above 80%. It is probably the best index to compare rebounding ability of two or more players because it takes into account both playing time and total Players with high free throw rate (especially if they also have high rebounds contested. number of attempts) are very efficient at drawing shooting fouls.

10 Green Grove Project Individual Performance – Assisting Efficiency

Assist to Turnover Ratio – Despite his impact on Zalgiris’ Assisted Field Goal Made (FGM) – Paulius Jankunas is the most game, Brandon Davies has the 4th worst rate among his efficient ‘executioner’. teammates. Players with high value in this category can be considered good spot It is used to evaluate ball handling and passing skills of a player. In shooters or finishers from close range, since the majority of assists end general, values greater than 1 are considered good. up in those kind of field goals.

11 Green Grove Project Individual Performance – Shooting Efficiency

EFG% (Effective Field Goal Percentage) - 7 players perform 2PA/3PA Ratio – Antanas Kavaliauskas & Paulius Jankunas are better than team’s season average (56.9). Arturas Milaknis is a more efficient than their guard teammates. Brandon Davies great scorer beyond 3 – point line, thus increasing his did not try any 3P shot during regular season. performance in this category. It could be used though together with FG%, EFG% or TS% to find out Similar to Field Goal Percentage, but adjusts for the fact that 3 - point whether a low value in any of those 3 indexes could be a result of field goals are worth 50% more than 2 - point field goals. Ideal to use increased number of 3PA instead of 2PA. to compare the FG% of two or more players.

12 Green Grove Project Individual Performance – Field Goal Efficiency

FG% After 25 Minutes of Playing Time - Despite his possible Close/Shots/Lay - up/Other FG% - Paulius Jankunas appears fatigue, Aaron White is very efficient in making field goals to be the most ‘productive’ player in FG%. after 25 min of playing time. The aim of this index is to provide useful insight regarding the This index is a way to measure impact of fatigue on a player. After a efficiency of a player in all above shooting categories. Therefore, certain amount of playing time (set to 25 minutes), most of them show coaching staff could plan accordingly to increase or reduce his signs of fatigue. attempts in order to maximize his potential FG%. ‘Other FG’ represents alley – oops and put-backs., and ‘Shots’ is shots beyond 3 meters from the basket and 3 – pointers.

13 Green Grove Project Individual Performance – Fouls Drawn

Fouls Drawn /Free Throws Made - Paulius Jankunas is the Charges Drawn – Kevin Pangos appear to be the most efficient player who draws the most fouls during the year, followed by player in drawing charges (4 times more that the average Pangos and Ulanovas. player) This index indicates player’s free throw efficiency, considering fouls This index indicates the defensive ability of each player. Players drawn. Players with high value, should be point of reference for with high value of charges drawn, have a proper position & team’s offensive plays. spacing on the court.

14 Green Grove Project Individual Performance – Efficiency of +/- Index

+/- Index – Paulius Jankunas & Kevin Pangos have the biggest +/- Index During Clutch Time - Paulius Jankunas & Aaron impact on Zalgiris’ game. The only 2 players who perform White have the biggest impact on Zalgiris’ clutch time game. better than teams’ season average. Kevin Pangos, despite his overall high +/- index performance, appears less efficient during clutch time. Equal to total team points scored minus opponent team points scored while a player was on floor. It can give a rough indication of his overall This index counts the overall efficiency of a player during clutch time, impact on team success. Positive values are considered good. as it takes into account both offensive and defensive awareness. Positive values are considered above average.

15 Green Grove Project Individual Performance – Clutch Time Player Efficiency

Clutch Time Percentage - Despite his impact on Zalgiris’ game, Clutch Time FG% - Aaron White appear to be the most Brandon Davies rarely participate in clutch conditions efficient player during clutch conditions (not including outliers) This index indicates if coaching staff trusts this player during clutch time, and if so, if an increase in his playing time leads to ending up This index indicates FG% players’ efficiency during clutch time. winning that game. Players with high FGM% or +/- during clutch time, should spend more time on floor during clutch time

16 Green Grove Project Team Performance

17 Green Grove Project 16 19 Team Performance – Opponent’s Difference

Opponent’s difference from average – In the majority of the categories, Zalgiris performs worse that its opponents. Strong point for Zalgiris is rebounding & weak point appears to be assists. This index demonstrates the difference between opponent’s performance and Zalgiris Kaunas in a series of statistical categories. For most of categories, this index should have a negative value. This way the coaching staff could know if team succeeded in keeping opponent below its average and moreover, which are its strengths and weaknesses.

18 Green Grove Project Team Performance – Index +/- considering different heights

+/- Index when Taller/Shorter/Same Height – Zalgiris performs better when its line-up is at the same height with the opponent. When its line-up is taller than the opponent, then it faces difficulties to get a positive result. Overall, shorter line-ups work better for Zalgiris. Team +/- index when average height lineup on floor is greater, less or same as opponent’s. This index can help to determine if team is expected to perform better or worse when any of the previous conditions is going to occur. Negative values in any of those 3 categories could mean that a higher or shorter lineup should maybe be deployed.

19 Green Grove Project Team Performance – Offensive vs Defensive Efficiency (1)

Defense vs G/SF/PF and C – Zalgiris’ weakest point in defense Second Chance Points and Offensive Efficiency - Zalgiris was its SF (extracted by season average) scored approximately 1.02 points per possession, but only 0.83 points per second chance possession. This index indicates FG% of opponent’s players playing in each of those positions. By analyzing the relevant results, coaching staff can This index indicates teams’ scoring efficiency after grabbing an get useful insight about the strongest position in terms of defense. offensive rebound. Zalgiris scored 244 second chance points in total from 293 offensive rebounds, and its average offensive efficiency was 101.99. If you mean zero value for offensive efficiency, it means 0 points scored per 100 possessions.

20 Green Grove Project Team Performance – Offensive vs Defensive Efficiency (2)

Opponent’s difference from average, 2FG% and 3FG% - Offensive/Defensive Efficiency – Zalgiris averaged more than Zalgiris kept each opponent below its 3FG% average just in 12 1 point per possession in 16 out of 30 games. Respectively, it games. conceded more than 1 point per possession only in 11 out of 30 games. At first look, its defence in 3-point range could not be considered amongst team’s strengths. This is also underlined by the season An estimation of total points scored per 100 possessions. It is very average value (+1.04%). Taking into consideration that the relevant useful for preparation when compared with opponent’s average value for 2FG% was -0.76% and that Zalgiris won only half of the defensive/offensive efficiency. games that opponent shot above its average beyond the arc.

21 Green Grove Project Team Performance – Fast Breaks from Steals & Turnovers

Steals Conversion Rate – In 16 games, Zalgiris perform better Turnovers - Zalgiris averaged 13.4 turnovers per game, than team’s season average (steal conversion rate = 59) leading the league this year. However, in terms of turnovers ending up to opponent’s steals Zalgiris is ranked 8th. This index indicates FG% of fast-break attempts coming from steals. High values (usually above 50-60%) reveal that team is quite efficient This index indicates team’s level of concentration during the game. at converting steals into points. High values (usually above 10) reveal that players are not concentrated and potentially give easy baskets to their opponents.

22 Green Grove Project Team Performance – Team Pace & Lead Percentage

Team Pace - Zalgiris team pace ranges from 64 to 80, with an Lead Percentage - Zalgiris led the score in 45.03% of total average of 69.9. It is evident therefore that Zalgiris is playing time. Although, this number is lower than 50%, it expected to perform better in slow paced games. managed to win 18 out of 30 games. Total number of possessions. A possession ends when a field goal/free Total percentage of playing time that team was leading the score. It throw is made or a turnover is committed. The most useful conclusion indicates whether team is able to maintain the lead it has and that can be extracted from this index is whether slow, normal or fast eventually win a game, or if it can also do a comeback and win a pace is better for the team to win a game. game where it was not leading for many minutes.

23 Green Grove Project Team Performance – Clutch Time Player Selection

+/- Index During Clutch Time, Combination of 2 Players – +/- Index During Clutch Time, Combination of 5 Players – K.Pangos & P.Jankunas was the best 2-player combination Clutch time line-up should consist of Pangos-Micic-Ulanovas- during clutch time. A.Milaknis & E.Ulanovas, had the worst White-Jankunas +/- clutch time index This is the most advanced and useful +/- index during clutch time. It is The purpose of this index is to provide a clear idea about the best a direct way to determine which lineup has the best team chemistry combinations in 2 different positions that can be deployed during and is more likely to perform better. clutch time (e.g. PG and C). This way, coaching staff could know which players form a better combination and can cover each other´s weaknesses.

24 Green Grove Project Conclusion This report was exclusively focused to in-Game season performance analysis of Zalgiris Kaunas. Our Data science team can also handle various other advanced requests in order to satisfy the needs of every team which strives to success. Those services include, but are not limited to: • Thorough analysis of specific player with his strengths and weaknesses. • In-Game Performance in various conditions (e.g. home/away games, during different time periods etc.). • Parametrization of indexes (using various filters such as game quarter or scoring margin). • Correlation between indexes and different sources. • Analyzing opponent's performance and creating tactical and athletic scouting reports. • Analyzing the full potential of the current squad and prepare specific transfer consulting services.

25 27 Intelligence as a service to identify the future stars!

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