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!
In this third installment of "Useful Basketball Stats" I wanted to present an easy to understand and very easy to implement 21st century stat. This is called EFFECTIVE FIELD GOAL PERCENTAGE. Everyone understands basic Field Goal (FG) % because it is just the total number of field goals made (FGM) out of total field goals attempted (FGA) so FG% = FGM/FGA........as easy as it gets so far. The one thing about the sport of basketball, and in this case specifically, field goals (excluding foul shots) there are two point values for field goals: 2 and 3. In soccer you don't get an extra point or goal if you score outside of 25 yards, it's the same to tap it in the net vs. blast a goal-kick into the opposing net. However, in basketball the area outside the arc accounts for one more point. Armed with this information there is a better way to understand your shooting average on field goals.
The reason eFG% tells a better story than FG% is that it takes into consideration that the 3pt make is worth 50% more than the 2pt make.
The formula is: eFG% = (FGM+(0.5 x 3PM))/FGA ------ 3PM = number of 3pt shots made
If you do not shoot any threes at all your FG% = eFG% and if you make 3pt shots than the formula gives you credit for it. FG% doesn't tell you enough because a 2ft shot is far different than a 22ft shot, and they shouldn't be valued the same.
If the math and short Excel formulas scare you just think, it's still just makes/shots taken, but add (0.5 x 3PM) to your total makes.
Hope this helps and gives you an understanding, having a shot chart with percentages shown from different spots on the floor is another useful way to differentiate FG% from different areas. Then as I said in a previous post you can also start to get specific with %s on contested vs. uncontested shots and start to get some answers about your offense. If you end up shooting a lot of contested shots in your offense maybe you need to investigate why. For your specific team what kind of shots do you want from what particular spots? What will put your best players in the best positions to succeed? All good questions to reflect on before, during, and after your respective seasons.
Thanks for reading!
In this second installment of useful basketball stats, I wanted to include a metric which isn't quite so familiar to people. Usage rate combines various values in a typical box score to estimate the number of team plays which are "used" by an individual player in the minutes they played. The values used in this estimate are rooted in the typical end of a possession which ends in three possible ways: a turnover, a field goal, or a free throw. Comparing these values, both team and individual, along with factoring in minutes played bring you the Usage Rate.
The formula is: 100 * ((FGA + 0.44 * FTA + TOV) * (Tm MP / 5)) / (MP * (Tm FGA + 0.44 * Tm FTA + Tm TOV)).
In an NBA example the leader in USG% last year 2016/2017 was Russell Westbrook at 41.7% So Westbrook used 41.7% of all the total possible plays in a game. These is obviously easy to understand qualitatively because Westbrook averaged a triple-double and was the at the center of all the action for the Thunder.
In a more relatable example for all levels of basketball......in my team's final game of the first semester my starting point guard had a great line of 18 pts, 8 rb, 8 ast - shooting 60% from the field, and playing 31 minutes. Her USG% that game was 22.1% So when she was on the floor she was involved in that percentage of the teams plays. The weakness of this stat is that it does not include assists in usage terms. You could have a player who fought for a rebound, dribbled through 3 people, and layed a drop pass to a teammate for a layup but technically she didn't "use" that play by this metric. There are other tools to compare this stat against such as Assist% and Offensive Rating which help to paint a clear picture about player effectiveness and efficiency. These analytics are tools, not silver bullet solutions, but they certainly tell you a lot more than a typical box score!
Some great resources for getting familiar with advanced stats and analytics are:
This is post is the first in a small 4-part series I am writing up to discuss basketball statistics, analytics and the useful application of different easy stats. Box-score stats really do not tell much of a story, but hopefully some of the ideas I am going to discuss will help to create a better narrative of what really happens in a game and over the course of a season.
The stat we are going to look at for Part 1 is a measure of offensive and specifically shooting efficiency. This stat is painfully simple but drills down to an essential focus on shooting efficiency. Pts/FGA is the stat. It is easy to understand and implement, It takes into account the fact that 3pt shots are worth more than 2pt FG's, so it inherently considers 3P%. It is different than the widely used Points Per Possession because instead of valuing each possession separately (maybe you fired up a few bad shots but got the offensive rebound to extend the single possession) it considers EVERY shot and the quality of them. Similar to Points Per Possession the more free throws you make the higher your Pts/FGA will be.
This stat is a great lens to look at team scoring efficiency from game to game, but it is also extremely useful in looking at individual player efficiency and effectiveness. If you have a player shooting lots of tough, contested jumpers (and not getting to the FT Line) it will show through in this stat very clearly.
Perhaps the most powerful application of this stat which I call "scoring efficiency" is to match it with charting shot quality. Many teams have rating systems for their shots; tagging a number to a specific shot type (I've seen this done several ways) for example: Open Layup = 9, Wide Open 3 - 7, Wide Open 2 (outside key) - 5, Contested 2 (outside paint) = 3, Contested 3pt = 2. You can play around with the valuing system to suit your concepts and style of play.
Without over-complicating things (a common fault for me) it would be hugely beneficial for teams to simply start tracking Open vs. Contested Shots, and then perhaps tagging onto that where on the floor these came from. That by itself would tell you a lot. For a great (and well linked article on this see: http://articles.basketballogy.com/2012/your-shot-chart-is-lying-to-you-and-what-to-do-about-it/
Credit for the Pts/FGA stat and the first place I saw it to Ben Taylor in his book "Thinking Basketball."
Stay tuned for Part 2 next week!
FOOTNOTE: As noted in Taylor's book Pts/FGA is the same stat as True Shooting%, (TS% just arranges the numbers in the form of a percentage). I would contend that Pts/FGA is a more tangible, easier to understand stat for players/coaches than True Shooting%. The simplicity of the stat combined with all the contributing factors that can go into "Pts" and "FGA's" can make for clear comparisons and easy application.
Recently I have been thinking a lot about using WHY questions to dig into analysis. In the past I have considered and used the Golden Circle concept quite often when analyzing situations related to basketball (among other things).
For instance in the case of a turnover you see when doing gametape --- The first level is WHAT HAPPENED?…..a turnover next HOW DID IT HAPPEN?…….let’s say a poor wing entry pass next level WHY DID IT HAPPEN?…..ball handler was off balance. It seems incomplete to stop there, although you certainly have gained valuable information. If you dig into the issue with another WHY then you really can uncover much more useful information. WHY WAS THE BALL HANDLER OFF BALANCE?…….poor core strength. It seems to me that using WHY as a tool to uncover issues can be very revealing. Not only does it help to eliminate bias, it also allows one to be very specific. If you never actually attack the root cause it will be hard to change. It is not nearly enough to just say "Stop Turning the Ball Over!" One has to be much more specific and understand the underlying causes.
This is definitely a concept I plan on expanding. See a Ricardo Semler TED talk below who I first heard his concept of “Three Why’s in a Row” while he was recently on the Tim Ferriss Show link also attached!
I listened to the Pure Sweat Basketball Podcast yesterday and caught up on an episode featuring University of Washington womens basketball head coach Mike Neighbors. In that episode Coach Neighbors discussed many things which coaches (often unknowingly) do wrong. He attempted to MYTH-BUST the age-old notion that "Defense Win's Championships." His argument was that it was Effective Field Goal % (eFG%) that wins championships. When you go back and study the metrics, it appears clear that offensive statistics correlated much more accurately with how teams finished in the playoffs and regular season in wins last year in the NBA.
When you take a look at Ken Pomeroy's site: www.kenpom.com you see a similar correlation between offense and defensive efficiency and how teams finished in NCAA Men's Basketball.
Final Four Teams are in BOLD:
You see many more of the perennial powerhouses populating that Top 10 in Offensive Rating, than you do Defensive Rating. Now this is not meant to make some grandiose point, the reading is simply to help readers question their assumptions and make people start to dig into cliche's or traditional statements. When you look a little further into the stats you see the winning teams are far from the bottom of the barrel when it comes to defense; they are typically very good. However the correlation (at least last season) seems to swing in favour of offensive metrics.
Thanks for reading, and check out the podcast with Coach Neighbors below!