Lottery features for use in machine learning algorithms or data analysis
Take a look at the examples folder. You will find results of how the features generated could give better insights to players.
TLDR: my own results
By using a slight variation of this, I was able to correctly predict 2 times a group of 3 balls (out of 10) of Trio Magic that contained the drawn middle number (2018). Thanks to that lottery earnings distribution, I was able to play tickets with all three numbers whithout ever loosing money. Basically, 1 winning ticket out of 3 was sufficient to break even (my goal). Better yet I still had a chance to win the jackpot.
In order to play all combinations offered by the prediction and claim the jackpot, I had to play roughly 300 tickets (that would have netted to 0 loss). Unfortunately, my student self was short on time (and $).
Other thing to note, I only played when the features were all pointing toward the given output. Such convergence of features only happened rarely. They occured in a 3-4 months interval the two times I played.
Other note, I tried a 3rd time to predict 3 numbers to see if the probabilities in such a convergence of feature would be exploitable / be generalized (my scientific side got carried). That time I did not buy any ticket (just checking prediction) but it failed. So I left that at the success rate of 2 / 3.
Theoretical results
Using Rapid Miner's Auto Model tool, a sample Naive Bayes model was created to see how the features can be distinguished from one another, which may lead to the ability to increase the potential of predictions.
The data used to train and test the model were generated using draw histories of 1/10 lotteries, like TrioMagic (Switzerland) and Joker+ (Belgium).
The goal was to predict whether the next ball would belong to a certain group (like we would try to predict the parity of the next draw). Hence, the target to predict was reduced to a binary output, which allowed me to try a classifier for this task. Here are the results of how each feature's correlation with each output class (represented in the graph by the True and False classes).
This graph shows us that this feature has a slightly different distribution depending on the class of the output to predict.
Here after, a feature with a distribution that that appeared to have a different distribution depending on the output class. This shows some correlation between the feature and the output. Even though it is known fact that correlation does not mean causation, it is still something worth noting.
..., where as other features do not show a lot of distinction between the classes.
So such features would be deemed irrelevant when trying to predict that output.
All in all, these graphs and results show that some features could be meaningfull when trying to predict whether or not the universe length would increase (which would point us to a group of symbols we might prefer to play for higher probabilities of winning).
See the example's README for more explanations.
Also, you can have a look at this discussion about the features and how we might want to use them.
PS: If you are a major in statistics/probability, I'd really appreciate some feed-back about the above annotated graph.
This repo is a specific snapshot of another project. On its own, it aims to generate features that are aimed to be used by a data scientist.
# Installs required Python Pip packages
pip install -r requirements.txt
# Install a custom helper library
pip install -U git+https://github.com/JeffMv/jmm-util-libs.git@v0.1.2.9.0Included data are from the triomagic lottery, which is a pick 1 out of 10 balls for each column lottery. You could substitute this dataset with one of a lottery with similar settings (pick 1 out of 10 for each column) and it would generate the features.
The graphs in the repo were generated with the such a setting. Especially, it used the univ-length-over10.tsv file, which is based on analyzing the number of different numbers that appeared in the last frame of 10 draws.
python eulolib/featuresUpdater.py --makeFeatures --gameId=triomagic --draws="data/example-inputs/TrioMagic-results.txt" --saveDir="triomagic"
# it writes in the input directory under the subfolder "triomagic"The generated folders are for each different column. You can remove the top 2 lines under the header of univ-length-over10.tsv and feed the file to an auto-model solution like RapidMiner's AutoModel to get the same kind of graphs that are shown in this repo.
IMPORTANT NOTE:
The other files named univ-ecarts-over10-andSupa20.tsv, univ-effectifs-over10-andSupa20.tsv or univ-parity-over10.tsv are on development so do not use them. I only created them as a stub for extending further. The only usable computated file are those called univ-length-over10.tsv.
Learning algorithms / Supervised learning
Files named univ-length-over10.tsv contain computed features (based on draw history) and labels for use in supervised learning algorithms.
The columns targetTrend and those starting with pred... (like pred2ndNext) are potential labels for a supervised learning algorithm. They each represent a way you can choose to tackle the problem. These axis can include choosing between Regression (like column targetTrend) vs Classification, predicting the next value vs predicting if a y value will appear within the next n draws. Another approach might be trying to predict the next value of one of the computed features (column predWillFollowIncreaseCapacity).
The columns starting with feat-... are columns containing computed features. You may try using them if you believe there are patterns.
Feal free to open an issue if you have any questions or to contact me regarding this project.
You may contact the original author for other licensing options.