Comments (3)
Previous season games for the team kobe
are taken into account whereas the 1st 3 get neutralised for new entrants into the league:
Hence, the calculations DO NOT seem correct. Note that this is not lagged here - so when comparing you need to look 1 date further above. For example, for the following dates it should look like:
2019-08-23: +3
2019-08-31: +8
from foostrat.
The issue is when neutralising the scores for new entrants via neutralise_scores
. The function performs its tasks as required. However, there seems to be an issue with the data.
kobe
is in 2 different leagues (Japan J1 League, Japan J-League) in the same season (2019).
from foostrat.
Leaving it for now since it's doing what it is supposed to do. Concluding that modelling and factor construction by league is preferred at this stage due to this and other issues.
from foostrat.
Related Issues (12)
- scheduling updates
- fooStrat as a pkg
- reverse engineer market odds
- source data processing failing HOT 1
- certificates for urllib requests
- whitespace issues in div during data load
- data source restructured
- matplotlib installation fails HOT 1
- create controls for factors
- resources HOT 1
- factor library development HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from foostrat.