A favorite paper of mine is the 1986 paper by Gardner and Altman regarding confidence intervals and estimation as a more useful way of reporting data than a dichotomous p-value: Gardner, MJ. Altman, DG. (1986). Confidence intervals rather than P values: Estimation rather than hypothesis testing. Brit Med J; 292:746-750. In this paper, Gardner and Altman discuss three main points for either moving away from or supplementing statistical reporting with p-values: Research often focuses on null hypothesis significance testing with the goal being to identify statistically significant results. Howev ..read more

I had the pleasure of speaking at the National Strength and Conditioning Association‘s (NSCA) National Conference this summer and while there I made it a point to attend the Sport Science & Performance Technology Special Interest Group meeting as well. One thing that immediately stood out to me was the number of questions raised specific to what types of technologies to purchase (e.g. “Which brand of force plates should we buy?”, “Does anyone have a list comparing and contrasting different technologies so that we can determine what would be best for us?”, etc.). While these are fine questi ..read more

This week, I had the great pleasure of being interviewed by my good friend and colleague Anthony Donskov for his High Performance Hockey Podcast. Anthony has done a tremendous job for the sports science and strength and conditioning community in his teaching, writing, and podcasting. He brings a wealth of knowledge from both the applied strength coach realm all the way through to his PhD work. In this podcast interview, Anthony and I discuss: Data analysis The PPDAC Framework for conducting research My criticisms of applied sport science The challenge of measuring hard things and things that ..read more

A friend of mine was downloading some force plate data from the software provider so that he could evaluate test data in a few of his athletes during return to play. The issue he was running into was that the different athletes all had different numbers of tests and different start and end testing times. The software exports the test outputs by date and he was wondering how he could normalize the dates to numeric values (e.g. Test 1, Test 2, etc.) so that he could model the date (since we can’t really use a Date in a regression model). I’ll be the first to admit that working with dates and tim ..read more

A common question that I get asked is for a simple way of displaying tables and plots together in the same one-page report. Most in the sport science space that are new to R will copy and paste their plot and table outputs into a word document and then share that with their colleagues. But, this creates extra work — copying, pasting, making sure you don’t mess up and forget to paste the latest plot, etc. So, today’s blog article will walk through a really easy way to create a single page document for combining tables and plots into a single report, which you can save to PDF or jpeg directly fr ..read more

I recently had the chance to work on a fun view point paper for the Journal of Orthopaedic & Sports Physical Therapy about ideas around analyzing data in the applied sports and rehab environments. While randomized controlled trials are considered a gold standard in medicine, the applied environment is a bit messy due to the lack of ability to control a host of factors and having the daily cadence and structure dictated by coaches and other decision-makers. Given these constraints, practitioners often lament that, “Research deals with group analysis but I deal with N-of-1!”. Indeed, it can ..read more

A colleague recently asked me a good question regarding some feature engineering for some data he was working with. He was collecting training load data and wanted to create a z-score for each observation, BUT, he didn’t want the most recent observation to be included into the calculation of the mean and standard deviation. Basically, he wanted to represent the z-score for the most recent observation normalized to the observations that came before it. This is an interesting issue because it makes me think of sports science research that uses z-scores to calculate the relationship between train ..read more

A recent discussion on Twitter spurred some thoughts that I had with respect to titles and roles in sport and in particular the title/role of Applied Sport Scientist. @ScientistSport posed the following question: It’s an interesting question to ponder. Given that sport science was originally born out of physiologists attempting to study human performance in Olympic sport athletes (which then eventually bled into team sport athletes) the question makes sense. Moreover, it seems like people generally think of sport science as something directed at helping the team “train better” – monitoring tr ..read more

One of the most frustrating aspects of building {shiny} apps is dealing with columns that have NAs when outputting tables. This is common in sport when dealing with players from different position groups who may have different stats that describe performance for those positions. Rather than writing a long series of if/else statements, I prefer to streamline the process by dropping those columns prior to returning the table of data. Not only does this make the app run smoothly but it also is easier to debug or add additional table information without having to deal with a lot of nested if/else ..read more

I’ve discussed using loops for a number of statistical tasks (simulation, optimization, Gibbs sampling) as well as data processing tasks, such as writing data outputs to separate excel tabs within one excel file and creating a multiple page PDF with a plot on each page. Today, I want to expand the loop function to produce separate SVG file plots and have R save those directly to a folder stored on my computer. The goal here is to have the separate plots in one place so that I can upload those files directly to a web app and allow them to be viewable for a decision-maker. NOTE: You can save the ..read more