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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Session 4: advanced analytics</title>
<meta charset="utf-8" />
<meta name="author" content="Ben Raymond, Adrien Ickowicz" />
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<textarea id="source">
layout: true
<div class="my-footer">
<div class="my-footer-box"><a href="https://openvolley.org/"><img style="display:inline;" src="extra/ovoutline-w.png"/>openvolley.org</a></div>
<div class="my-footer-box"><a href="https://https://volleyball.ca/"><img src="extra/vc-w-wide.png"/></a></div>
<div class="my-footer-box"><a href="https://untan.gl/"><img src="extra/su_title-w.png"/></a></div>
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---
class: inverse, logo, center
<img src="extra/3logo2.png" style="width:65%; margin-bottom:50px;" />
## Session 4: Advanced analytics
### Adrien Ickowicz, Ben Raymond
##### with valuable contributions from many others...
---
## What are we going to talk about
- Statistical modelling
- descriptive statistics vs inference
- Is causality reachable ?
- Inference via simulation vs Inference via statistical modelling
- Simulating matches
- the <span class="pkg">volleysim</span> package
- the <span class="pkg">bandit</span> package
- Statistical (inference-based) models
- Use the example of the effect of player height on passing performance
- the effect of serve speed on outcome? Serve error vs breakpoint ?
---
## Statistical modelling
#### descriptive statistics vs inference
From wiki:
A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics.
Example of descriptive statistics:
* Number of serve errors per set
* Break-point rate per set
Example of inference:
* How do these two quantities relate to one another?
---
## Statistical modelling
#### Causality
<img src="extra/xkcd_causality.png" style="width:70%; margin-bottom:50px;" />
credit: https://xkcd.com/
---
## Statistical modelling
#### Causality
<img src="extra/xkcd_causality_v2.png" style="width:90%; margin-bottom:50px;" />
credit: https://xkcd.com/
---
## Statistical modelling
#### Causality and correlation
https://www.tylervigen.com/spurious-correlations
So what is the problem?
- An increase in the number of people at the beach causes a higher number of ice cream sales
- An increase in the number of people at the beach causes a higher number of shark attacks
- Therefore, people need ice cream for comfort after a shark attack; or is it that sharks attack people after they ate ice cream because they taste better?
---
## Statistical modelling
#### Inference via simulation vs Inference via statistical modelling
When do we use either:
- When the world can be simplified without loss of (too much) information, we can simulate;
- Example: Can we simplify a volleyball game if we only want to know who is going to win? Anyone has an opinion?
- When the world is too complex, and we have no clue or the process is convoluted, we may want to try something simpler at first;
- Example: Are tall players better passers than short players?
In a nutshell, when you cannot narrow down the factors that influence the uncertainty to a handy few, forget about simulation.
---
## Simulating matches
#### the <span class="pkg">volleysim</span> package:
- vs_estimate_rates
- vs_example_file
- vs_match_win_probability
- vs_simulate_match
- vs_simulate_match_beach
- vs_simulate_match_mc
- vs_simulate_match_theor
- vs_simulate_set
- vs_simulate_set_mc
- vs_simulate_set_theor
- vs_theoretical_sideout_rates
---
## Simulating matches
#### the <span class="pkg">volleysim</span> package:
A quick overview: https://untan.gl/r-package-simulating-volleyball.html
Our turn. Open `s4-example-volleysim`.
---
## Simulating matches
The bandit functions in the <span class="pkg">ovlytics</span> package
- ov_simulate_setter_distribution
- ov_plot_ssd
- ov_plot_sequence_distribution
- ov_plot_distribution
- ov_create_history_table
What is this? See https://untan.gl/setter-choice.html
Our turn. Open `s4-example-bandit`.
And if you dont want to code... https://apps.untan.gl/bandit/
---
## Do you know...
More analytics articles:
https://untan.gl/index.html
---
## Statistical models
#### Player height and passing performance
Question: Does height influence the passing quality?
Open `s4-player-height-passing`.
---
## Statistical models
#### Serve agression, speed and outcome
Question 1 : Should we ban serving errors ?
Question 2 : Is there an optimal serve speed for which the expected break-point is maximum ?
Open `s4-serve-speed-example`.
---
## Statistical models
#### Identifying a defensive system
A blog post: https://untan.gl/identifying-defensive-systems.html
An app: https://apps.untan.gl/volleydef/
<img src="extra/defensive_zones.png" style="width:90%; margin-bottom:50px;" />
---
## Statistical models
#### Identifying a reception system
Can we do the same with reception?
An app also: https://apps.untan.gl/volleypass/
<img src="extra/reception_zones_P1.png" style="width:70%; margin-bottom:10px;" />
WMCH 2018 - Italy - P2. What is happening?
</textarea>
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