Saturday, March 29, 2014
No-Holds Barred Interview with Carl Valle
- Sport Psychology- The “soft science” is the strongest variable because it’s about dealing with the human element of emotion and behavior. One can argue it’s about connecting but I think it’s about placing the athlete in a mind state of doing what it takes to succeed with conscious decisions. Coaches should be aware of the three Ps, personalities, perspectives, and what has worked in the past. Motivation is also a part here, and tapping into the athlete’s goals is about bridging behavior to belief.
- Education- Another three letter approach using the three Vs in monitoring or athlete education in general. Explaining just enough information, be it verbal (dialogue) or visual (infographics) is extremely helpful to get the athlete to value the data. Talking about parasympathetic changes or wattage in jumps is not the language of athletes (although they are evolving fast thanks to the internet) but the use of creative analogies and layman’s terms is helpful here. Numbers are ok as well, since solid objective feedback of the clock, the bar, and the tape all ensure the athletes know what is going on.
- Salesmanship- Having athletes buy in requires coaching marketing and campaigning a cultural reset. Sales is easy when you love what you are doing. If monitoring is a pain to the coach, no way is the athlete going to leave feeling positive either. If you don’t use the data or don’t demonstrate
- Trust- A combination of experience, history of results, knowledge, and the coaching relationship determines everything. Athletes want to know your motives because caring is hard acquire in large group situations. You can claim to care when you barely know names or spend time with someone. Sharing your motivation for helping them gives the athlete a perspective to how much effort you plan to invest in and what your limits are. Is the coach in it for the passion and art or financial reward and ego?
- MyoAnalytics (Tensiomyography and myoton metrics) – This data is more fleeting and variable and can be used to do preseason screening and monitoring of manual therapy and training interventions. Also combined with Player tracking data and motion capture, a real profile of athletes can be made. Movement signatures with force plates or accelerometers is like painting with a wide brush for houses and trying to do a portrait. One high profile team is including military grade thermography surveillance cameras as early warning tools and then validate with muscle diagnostics. When patterns calibrated by research and estimated coefficients are added, the early warning system alarms or tags the event.
- Pressure Mapping (In-shoe and barefoot)- Injuries from the plantar fascia to lower back can be linked to ground reaction forces that may not a good fit with the athlete. Remember athletes are now over-competed and under prepared, a death sentence coaches and medical teams are trying to manage. Asymmetries can be absorbed from the amazing nervous systems of athletes, but the workhorses are muscles that may not have enough ability to handle rapid eccentric forces. Pressure mapping alone has merit, but sEMG and motion capture connects the dots. If you look a professional soccer they are on a runaway train to imploding with higher outputs of both speed and conditioning, and small problems may be fine driving around a lazy Sunday to church, but on the autobahn going 180 kilometers per hour, alignment issues are sometimes exponentially problematic. One example of this is an athlete that had one foot injured as a child with morphological and structural changes that caused him to drift 10% while sprinting. For every ten meters he veered a meter. As he got faster he experienced muscle strains and had him get pressure mapped and the COP trajectory of foot strike was radically different and this caused the drift each step. Combined with the analytics run on his fiber testing and jump tests, his fatigue pattern didn’t create a solid buffer zone. The patterns of muscle status cross validated the predicted patterns from EMG and motion capture, and the pressure mapping identified the potential cause.
- I would like to see MOXY or a similar product used during speed tests and player tracking devices more in order to see how offensive linemen and defensive linemen fatigue specifically in American football. GPS and accelerometer data is like Jackson Pollock paintings, it’s popular and people believe they interpreted it right but I question the value of it’s use. We need more muscle fatigue information with sEMG and other data to see what is going on in the trenches. Also, let’s see how well those breathing “workouts” are transferring between reps and between sessions. Certainly we should se some changes in recovery that is showing up somewhere.
- As for the CML device I want to see it cross-validated with CNS testing from Omegawave and have both compared to some intensive analysis. While central fatigue and the peripheral fatigue are different, both have the same general purpose of seeing nervous system fatigue interact with performance incompetence and injury from fatigue. I love jumping tests but can’t do them daily so we want a passive way to look at general explosive ability status of the body. Perhaps some field tests mixed with some non-voluntary testing as well as POMS like scores can show what is more precise, assuming both are valid and reliable.
Tuesday, March 25, 2014
How to [pretend to] be a better coach using bad statistics
Here is a simple scenario from practice: Coach A uses YOYOIRL1 test and Coach B uses 30-15IFT (for more info see recent paper my Martin Buchheit, which also stimulated me to write this blog) to gauge improvements in endurance
Coach A: We have improved distance covered in YOYOIRL1 test from 1750m to 2250m in four weeks. That is 500m improvement or ~28%
Coach B: We have improved velocity reached in 30-15IFT from 19km/h to 21km/h in four weeks . That is 2km/h improvement or ~10%
If you present those to someone who is not statisticaly educated he/she might conclude the following:
- Coach A did a better job, since the improvement is 28% compared to 10% of Coach B
- YOYOIRL1 test is more sensitive to changes than 30-15IFT
As a coaches, we needs to report to a manager(s), so which one would you prefer reporting? 28% or 10%? Be honest here!
Unfortunately, we cannot conclude who did a better job (Coach A or Coach B), nor which test is more sensitive (YOYOIRL1 or 30-15IFT) from percent change data. A lot of managers and coaches don't get this. At least I haven't until recently.
What we need is Effect Size statistics, or Cohen's D. But for that we need to know variability in the groups, expressed as SD (standard deviation). Let's simulate the data and use usual SDs for YOYOIRL1 and 30-15IFT
require(ggplot2, quietly = TRUE) require(reshape2, quietly = TRUE) require(plyr, quietly = TRUE) require(randomNames, quietly = TRUE) require(xtable, quietly = TRUE) require(ggthemes, quietly = TRUE) require(gridExtra, quietly = TRUE) set.seed(1) numberOfPlayers <- 150 playerNames <- randomNames(numberOfPlayers) # Create YOYOIRL1 Pre- and Post- data using 300m as SD YOYOIRL1.Pre <- rnorm(mean = 1750, sd = 300, n = numberOfPlayers) YOYOIRL1.Post <- rnorm(mean = 2250, sd = 300, n = numberOfPlayers) # We need to round YOYOIRL1 score to nearest 40m, since those are the # increments of the scores YOYOIRL1.Pre <- round_any(YOYOIRL1.Pre, 40) YOYOIRL1.Post <- round_any(YOYOIRL1.Post, 40) # Create 30-15IFT Pre- and Post- data using 1km/h as SD v3015IFT.Pre <- rnorm(mean = 19, sd = 1, n = numberOfPlayers) v3015IFT.Post <- rnorm(mean = 21, sd = 1, n = numberOfPlayers) # We need to round 30-15IFT to nearest 0.5km/h, since those are the # increments of the scores v3015IFT.Pre <- round_any(v3015IFT.Pre, 0.5) v3015IFT.Post <- round_any(v3015IFT.Post, 0.5) # Put those test into data.frame testDataWide <- data.frame(Athlete = playerNames, YOYOIRL1.Pre, YOYOIRL1.Post, v3015IFT.Pre, v3015IFT.Post) # And print first 15 athletes print(xtable(head(testDataWide, 15), border = T), type = "html")
|4||Venegas Delarosa, Destinee||1640.00||1800.00||19.50||21.50|
|9||Martin Dean, Jillian||1440.00||1960.00||21.00||22.00|
To plot the data and to do simple descriptive stats we need to reshape the data from wide format to long format using reshape2 package by Hadley Wickham
# Reshape the data testData <- melt(testDataWide, id.vars = "Athlete", variable.name = "Test", value.name = "Score") # And print first 30 rows print(xtable(head(testData, 30), border = T), type = "html")
|4||Venegas Delarosa, Destinee||YOYOIRL1.Pre||1640.00|
|9||Martin Dean, Jillian||YOYOIRL1.Pre||1440.00|
From the tables above it is easy to see the difference between wide and long data formats.
Let's calculate simple stats using plyr package from Hadley Wickham (yes, he is a sort of celebrity in R community) and plot them using violin plots, which is great since they show the distribution of the scores
# Subset YOYOIRL1 tets ggYOYO <- ggplot(subset(testData, Test == "YOYOIRL1.Pre" | Test == "YOYOIRL1.Post"), aes(x = Test, y = Score)) ggYOYO <- ggYOYO + geom_violin(fill = "red", alpha = 0.5) + theme_few() + stat_summary(fun.y = mean, geom = "point", fill = "white", shape = 23, size = 5) # Subset 30-15IFT tets ggIFT <- ggplot(subset(testData, Test == "v3015IFT.Pre" | Test == "v3015IFT.Post"), aes(x = Test, y = Score)) ggIFT <- ggIFT + geom_violin(fill = "steelblue", alpha = 0.5) + theme_few() + stat_summary(fun.y = mean, geom = "point", fill = "white", shape = 23, size = 5) # Plot the graphs grid.arrange(ggYOYO, ggIFT, ncol = 2)
# Calculate the summary table testDataSummary <- ddply(testData, "Test", summarize, N = length(Score), Mean = mean(Score), SD = sd(Score)) # Print the summary table print(xtable(testDataSummary, border = T), type = "html")
From the table above we can calculate the percent change.
YOYOIRL1.Change <- (testDataSummary$Mean - testDataSummary$Mean)/testDataSummary$Mean * 100 v3015IFT.Change <- (testDataSummary$Mean - testDataSummary$Mean)/testDataSummary$Mean * 100 print(xtable(data.frame(YOYOIRL1.Change, v3015IFT.Change), border = T), type = "html")
But as mentioned in the beginning of the post, percent change is not the best way to express change and sensitivity of the tests (although it is great to impress the managers or your superiors, or claim that your test is more sensitive).
What we need to do is to calculate effect size (ES). ES takes into account the difference between the means and SD (in this case of the Pre- test, but it can also use pooled SD).
YOYOIRL1.ES <- (testDataSummary$Mean - testDataSummary$Mean)/testDataSummary$SD v3015IFT.ES <- (testDataSummary$Mean - testDataSummary$Mean)/testDataSummary$SD print(xtable(data.frame(YOYOIRL1.ES, v3015IFT.ES), border = T), type = "html")
From the data above we can conclude that they are pretty similar and that 30-15IFT might be a bit more sensitive (or the Coach B did a better job).
Anyway, to summarize this blog post - start reporting ES alongside with percent change. If someone claims high improvements in testing scores to show how great coach he is, or how great his program is, ask to see ES or the distribution of the change scores or Pre- and Post- tests. Besides we need to also ask for SWC and TE, but more on that later.