Uptake of analytical metrics is below par. The main gatekeepers are the coaching staff. The more intuitive the metric, the wider the gate is opened. Yet fight against intuition and the gate swings shut. An understandable situation.
Increasing adoption and prising that gate open is important and can be achieved by:
 Making tools that improve the processes of coaching and game preparation
 Automatic generation of video clips based on metrics
 Improving our communication and interpersonal skills

Including coaches in the development of metrics
The fourth can be very powerful, it increases twoway communication, application and ownership. It’s a sweet spot that is under utilised. Yet others would raise warning flags.
We need a theoretical and conceptual framework before we can make sense of this tracking data. That’s what I say for the event data in the article you’re linking, but the need is even more critical in the case of tracking data. What are the important variables? What is the unit of analysis? We simply do not know. One way around the problem would be to borrow coaching concepts, but the work on event data was most successful when it challenged preconceived ideas about what’s important and what’s not, so I’m not a fan even though it would definitely help with the adoption
Marek Kwiatkowski
In Interview
I value the challenging of the preconceived. Gaining edges in the unknown or undervalued. Yet we will get there quicker with adoption and increased influence. Building in expert knowledge directly into models and metrics is a powerful tool. Although we may not discover football’s “Move 37“.
Tracking data is heavy. Each game consisting of 1,835,000 lines and 137,825,000 characters. Clubs are only now starting to investigate it’s possibilities. Modelling insights from 5 seasons of such data is computationally expensive.
My hypothesis: Inject expert football knowledge into the core of tracking data modelling. This injection leads to quicker and computationally cheaper insights. ShotSmash was an experiment which gently prodded my hypothesis.
ShotSmash asked ‘experts’ to decide which of a pair of scoring opportunities was ‘better’. The voting results fed into an Elo Rating system which over the course of 12,000 votes gave each of the 939 shots had an final Elo Rating. Not a new idea, thanks Zucks.
Similar xG values but very different situations
For each shot I calculate the Expected Goal Value using
Ben Torvaney’s xG model (there are more accurate models but this is the one I have access to).
Then let’s run a logistic regression with the goal result as the outcome and Elo and xG as predictors.
So both predictors are significant, but Elo Rating more so. Let’s investigate if this difference is significant by calculating the odds ratio for both predictors.
Elo : 2.47596756
xG : 1.24370437
Both are greater than 1, great! Let’s calculate the confidence intervals of the odds ratios of both predictors.
Elo
2.5%: 1.76831294
95%: 3.53230726
xG
2.5%: 0.97506327
95%: 1.57074806
There is no overlap therefore confirming a significant difference in predictive accuracy between the variables. Bingo!
In terms of model comparison; the Elo model is also significantly better than the xG model at predicting goal/no goals. The Elo model predictions being 23% more accurate than xG. This is predictable as the Elo model makes use of tracking data.
The Shotsmash model is far from being ready to implement and was just a bit of fun. Hopefully it sparks some thoughts in others of how they can incorporate coach expert knowledge into the process of building metrics.