22:
154:- Measures the division of labor, or the expertise in moving the ball to the player with the best shooting percentage. According to Fewell et al. It can be interpreted as an average change in potential shooting percentage per pass. The metric is calculated as a sum of the differences between the shooting percentages of the nodes at the ends of each edge
776:- The metric is calculated by mapping the bipartite player network. Players are connected if they were a part of one team. The links are weighted by how successful was the team, where the players played together. Then node centrality measures are compared to the reference centrality distributions for each node obtained by
137:
researchers led by
Jennifer H. Fewell. Using 2010 NBA first round playoff data, they constructed the networks for each team using players as nodes and ball movement between them as links. They distinguish the trade-off between not necessarily mutually exclusive division of labor and team's
924:, who while working for the data visualization company Ayasdi, mapped the network of one season NBA players linking them by the similarity of their statistics. Then, based on node clusters players were grouped into 13 positions.
896:
630:
397:
770:- The number of times the player (node) was involved in the successful play divided by the number of times the player was involved in the unsuccessful play. The metric is obtained from the team play by play network.
755:
239:
517:
404:
Combined low clustering and high degree centrality mean that the defense can put double team on the dominant player, since without him ball team experiences problems in moving the ball.
97:
are determined by individual attributes. In contrast, these network based analytics are obtained by constructing a team or league level player networks, where individual players are
1133:
1132:
Piette, J, Pham, L. and Anand, S. (2011) “Evaluating
Basketball Player Performance via Statistical Network Modeling,” in Sloan Sports Analytics Conference, (Boston, U.S.A.),
1105:
Fewell J.H., Armbruster D, Ingraham J, Petersen A, Waters JS (2012) Basketball Teams as
Strategic Networks. PLoS ONE 7(11): e47445. doi:10.1371/journal.pone.0047445
125:
etc. This approach enriches the analysis of basketball with new individual and team level statistics and offers a new way of assigning position to a player.
789:
430:. Each individual is assumed to have a skill curve f(x), which is declining in the number shots taken. Individual maximization of the efficiency yield
522:
287:
144:- A measure of unpredictability and variation in teams offense, higher entropy meaning more variation. It is calculated as aggregated individual
122:
1134:
http://www.sloansportsconference.com/wp-content/uploads/2011/08/Evaluating-Basketball-Player-Performance-via-Statistical-Network-Modeling.pdf
276:. It measures how interconnected are the players, whether the ball moves via one node or whether in many ways between all the players.
635:
906:
is the calculated centrality score, J - number of iterations. High p - values indicate under-performance, low - over-performance.
282:- Similarly to the previous metric, it measures if there is one dominant player in the team. It is calculated by the formula
160:
40:
32:
145:
58:
1117:
Brian
Skinner (2011) The Price of Anarchy in Basketball, Journal of Quantitative Analysis in Sports 6(1), 3 (2010),
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where deg(v) is the degree of the node v, deg(v) is the highest degree node, V is the number of nodes.
81:
comprise a various recent attempts to apply the perspective of networks to the analysis of basketball.
1175:
912:- A player is under-utilized by the time if he has a low degree centrality, but is over-performing
423:
134:
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780:- based randomization procedures and p - values are calculated. For example p - value of player
148:, where unpredictability is measured as uncertainty of the ball movement between any two nodes.
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The difference between these two constitute the teams deviation from the maximum potential.
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analyze individuals independently of their teammates or competitors and traditional player
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unpredictability that are measured by Uphill downhill flux and Team entropy respectively.
8:
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891:{\displaystyle p_{i}={\frac {\sum _{k=1}^{J}(I|\pi _{i^{*}k}\geq \pi _{i}^{0})}{J}}}
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Player who is above average in both offense and defense, but doesn't excel in any.
416:- Number of passes per unit time. It measures how quickly the team moves the ball.
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1034:
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625:{\displaystyle {\frac {dF}{dx_{1}}}={\frac {dF}{dx_{2}}}...={\frac {dF}{dx_{i}}}}
72:
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Those valued for blocking and rebounding, but with low average points scored.
392:{\displaystyle C_{D}=\sum _{v\in V}{\frac {deg(v^{*})-deg(v)}{\mid V\mid -1}}}
1164:
105:. Then, the metrics are obtained by calculating network properties, such as
1039:
1107:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0047445
964:
Player that is above average in shot attempts and points scored per game.
1049:
920:
New basketball positions were classified by
Stanford University student
114:
76:
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Ones that are so good and exceptional that could not be categorized.
1118:
970:
Those who play few minutes and don't have large impact on the team.
1008:
Those with above averages in most of the statistical categories.
1002:
Players that at both good and offense and defense in the paint.
952:
and stealing the ball, but is average in scoring and shooting.
750:{\displaystyle F=x_{1}f(x_{1})+x_{2}f(x_{2})...+x_{3}f(x_{3})}
422:- Using players as the nodes and ball movement and links and
133:
The biggest contribution to the team level metrics came from
1071:
https://www.wired.com/2012/12/basketball-network-analysis/
251:
is the probability of the link between players i and j, x
1152:
234:{\displaystyle F=\sum _{i\neq j}p_{ij}\ (x_{i}-x_{j})}
1128:
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whereas to maximum efficiency is achieved by solving
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Similar, but a bit worse than NBA 1st-Team players.
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https://www.wired.com/2012/04/analytics-basketball/
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391:
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976:A big man and a ball handler with above average
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990:Player with high scoring and rebound averages.
1149:"Analytics Reveal 13 New Basketball" Positions
1020:Similar, but worse than NBA 2nd-Team players.
512:{\displaystyle f(x_{1})=f(x_{2})...=f(x_{i})}
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1066:https://www.youtube.com/watch?v=oz1uQi_epAo
934:and ball handling, but has low averages of
426:as efficiency, analogy can by made to the
420:Deviation from maximum operating potential
101:connected by the ball movement or by some
1082:
59:Learn how and when to remove this message
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128:
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902:is the reference centrality score, π
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31:tone or style may not reflect the
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1119:https://arxiv.org/abs/0908.1801v4
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263:are their shooting percentages.
41:guide to writing better articles
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410:- Number of passes per play.
272:- A direct application of a
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930:Player that specializes in
270:Team clustering coefficient
84:
10:
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948:Player who specialized in
968:Role-Playing Ball-Handler
424:true shooting percentage
266:Other measures include:
135:Arizona State University
1000:Scoring Paint Protector
946:Defensive Ball-Handler
928:Offensive Ball-Handler
892:
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774:Under/over performance
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280:Team degree centrality
274:clustering coefficient
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1171:Basketball statistics
1153:https://www.wired.com
962:Shooting Ball-Handler
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768:Success/Failure Ratio
763:Individual statistics
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129:Team level statistics
103:measure of similarity
91:basketball statistics
984:attempted and made.
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988:Scoring Rebounder
982:three point shots
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35:used on Knowledge
33:encyclopedic tone
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49:December 2019
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89:Traditional
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1050:APBRmetrics
1018:Role Player
1165:Categories
1077:References
119:clustering
115:centrality
77:basketball
950:assisting
898:, where π
866:π
862:≥
852:∗
843:π
811:∑
778:bootstrap
381:−
378:∣
372:∣
349:−
341:∗
313:∈
306:∑
216:−
179:≠
172:∑
95:positions
79:analytics
1030:See also
978:rebounds
632:, where
245:where p
123:distance
85:Overview
932:scoring
111:density
940:blocks
936:steals
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