Readme

Author: glandfried

README.md

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 ## TrueSkill Through Time: the Julia, Python and R packages.
 
 Humans develop complex skills through time.
-Estimating them is essential in education, labor and sports systems.
-However, it is a sensitive issue when they are intended to be used to make decisions that may impact individuals.
+Knowing how individual abilities change is essential in a wide range of activities.
+Most estimators implemented for the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates.
 
-None of the widely used skill estimators, such as TrueSkill, Glicko and Item-Response Theory, correctly models the temporal aspect, which prevents having both good initial estimates and comparability between estimates separated in time and space.
+TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network.
+This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer.
 
-TrueSkill Through Time corrects those biases by modeling the entire history of activities using a single Bayesian network.
-The use of an efficient algorithm, that requires only a few linear iterations over the data, allows scaling to millions of observations in few seconds.
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-In this paper we offer the first packages for `Julia`, `Python`, `R`, together with its scientific documentation, supporting the use of a reliable learning curve estimators.
+To support the use of reliable learning estimators, we provide the first implementations of TrueSkill Through Time for  `Julia`, `Python` and `R`.
+A complete scientific documentation allows scientists to make sense of all epistemological and technical aspects of the estimation process.
 
 ### Scientific article
 
@@ -28,11 +27,11 @@ You can find the full scientific documentation of TrueSkill Through Time package
 ### The History of the ATP
 
 The following figure presents **the learning curves of some famous players in ATP history**.
+A one-point difference between skills is equivalent to 76% probability of winning.
 
 ![atp](static/atp.png)
 
 Those who know the history of tennis will be able to recognize the periods of crisis, stability and success of the players.
-
 There is a relative coincidence between skill and who is at any given moment at the top of the ATP rankings, it is possible to observe the effects of injuries, and even the effect of emotional slumps such as those that Aggasi and Djockovic had.
 
 It is interesting to see that the skill of tennis players did not increase so much over the years: on the contrary the players of the 1980s were more skilled than those of the 1990s, and reached a skill similar to what Federer, Nadal and Djokovic had in 2020.
@@ -44,9 +43,6 @@ The **models commonly used in industry and academia** (TrueSkill, Glicko, Item-R
 ![atp](static/atp_trueskill.png)
 
 The advantage of TrueSkill Through Time lies in its temporal causal model, that links all historical activities in the same Bayesian network, which guarantees both good initial estimates and the temporal and spatial comparability of the estimates.
-We have shown that this procedure converges quickly even on large databases.
+We have shown that this procedure converges quickly on large databases.
 
 With this article we promote their adoption by providing the first packages for Julia, Python and R, together with the complete scientific documentation.
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