Great example here of a step up screen by the Cleveland Cavaliers. By running a side, spaced-out, step up ball screen you put the help in a very precarious position. In the video and the typical rotation is for x3 to rotate over leaving many possible openings. Crowder reads the situation and runs to the rim for an easy lay-in.
Typically in fitness testing, measurements are kept on an individual basis and single players are held accountable for their current levels, their hopeful progressions, and their maintenance of athleticism, strength, and cardio levels over time. I want to suggest a similar but alternate lens to look at this testing and measuring process through.
In considering motivation on an extrinsic and intrinsic level, I am openly wondering if instead of measuring individual players; that getting standards for the whole group may be more effective. A very applicable and basic example of this would be in-terms of the Yo-yo or Beep Test. In going from individual scores to group scores you would go from firmly considering only single player scores to a group total score. This score could be obtained by considering an early season total score, and then re-evaluating at different points throughout the season, or after significant breaks (after final exam break, or in post-off season). In this example if you wanted an average beep test score of 10.0, and you have 12 players, your group total score would be 120. Rather than only worrying about themselves and their own scores players now would have a greater cause to work towards which is the total group score.
My way of testing this year before our extended break for first semester exams was a timed sprinting series. I have been seeking testing which is more game-like and to this point I think this drill I learned from Alan Stein at Pure Sweat is the best I have come across. For our test we run lengths in three groups. One length of the court is considered "1." In three even groups we run 1-3-5-7-9 sprints continuously. Group 1 runs 1, then Group 2, then Group 3....after completing the first sprint, Group 1 runs 3, then Group 2 runs 3 sprints......and so on. The next group cannot leave until the final member of the running group has crossed the line, and we time the total it takes to complete. This is a great running drill and lends itself to all sorts of alterations such as making it a true pyramid and working your way back down to 1 sprint, or as Alan Stein does and move the same running drill into and half and then a quarter court iteration after the full sprints are completed. I have the total time, and will be re-evaluating in the first practice after the Christmas Break. It is a little bit of action research to see if perhaps group pressures are more effective then just individual pressures for performance. We shall see!
I would love to know other basketball specific aerobic/anaerobic testing coaches do, if you feel so inclined leave some ideas in the comments below, or contact me via social media!
In the final installment of Useful Basketball Stats, I want to discuss 'Assist Percentage' (AST%). This stat as you can tell by the formula below estimates for the minutes played by an individual, what percentage of the teams FG's come from that player's direct assists. The formula is as follows:
Assist Percentage=100*Assists/(((Minutes Played /(Team Minutes/5)) * Team Field Goals Made) – Field Goals Made)
As you can tell by reading into the formula it takes the minutes played over the total possible minutes played which in FIBA would be 40, and then accounts for the teams total field goals that game minus the individual player's field goals, which obviously they could not assist on. The painstakingly 100% accurate way to calculate this would be to go through the tape or play-by-play stats and see when the player is on the floor of the teams field goals which were assisted by that player and then divide it into a total percentage. Therefore this stat is an estimate, but it's a pretty good one. In 1990-1991, John Stockton had an AST% of 57.5%. On my college team, my 4th year point guard has an AST% of 30.2% with no one else on the team being over 20%. This makes it very clear from a play-making and distributing perspective we will have a big gap to fill next year
The best thing about the analytics I've discussed over the past few weeks is that they help to tell a better story of the game, your players, and your team as a whole. The standard box score is too limited an explanation of what is really taking place on the court. I hope this has at least opened some eyes to the analytics side of basketball stats and perhaps shown they are quite accessible and should not be intimidating. Leave any questions or points of discussion in the comments below!