Ninja Tipster from


79 published tips
79 decided tips

Total profit
-4.070 units

I have been a football fan my entire life, and have been involved in the football betting industry from the moment I turned 18. I have a passion for statistics, especially in sports, and have been studying for a degree in Mathematics with a focus on statistics for the past 3 years. Having a large range of statistical techniques in my arsenal has proven a great advantage when it comes to betting, as it helps to take some of the randomnesses out of the sport.

I started out mainly as an in play tipster, as my ability to collate and interpret in-game stats at a rapid pace gave me a major upper hand over a lot of other in play tipsters. I still work as one over 3 years later, but I have also since moved more into the field of previewing games and giving pre-match predictions, with great success in both fields.

In October 2018, I began work on a statistical model to predict the Premier League, collecting data from previous seasons - from scores and form patterns to home advantages and in-game patterns - and testing out several different techniques, from a basic Poisson model, to more complicated Dynamic Bivariate models, and incorporating many other factors as I went. In January 2019, I settled on a model which I believe produces the best results, and can consistently not only predict correct results but also turn over a large profit against the bookmakers. The first 8 weeks of testing this model resulted in an ROI of 22%, so if it can keep up anywhere close to this level of consistency, it will be an extremely valuable and profitable asset to any bettor.

Service Info:
The model is currently only molded to the Premier League, however, in June 2019, there are plans to expand this model to work on other leagues, starting with summer leagues like the Swedish Allsvenskan and the Irish Premier Division, to ensure I can continue to provide excellent tips 12 months a year. After that, the model will be expanded to other major European leagues, such as La Liga, Bundesliga, Serie A and Ligue 1.

Types of bets:
Bets will mostly be taken from 3 main markets- Full-Time Result, Both Teams to Score and Match Goals - including any combinations of those markets too, such as Win & Both Teams to score or Double Chance. There are plans to potentially widen the scope of the model's prediction, but while it is performing at the rate it is currently, there is not a great deal of need to expand the markets. As the common saying goes: "Don't fix what isn't broken!"

Average Win Rate:
The odds of the bets provided by the model can fluctuate quite a bit, but I expect a strike rate of around 40%, considering our average odds are around 2/1 (3.00).

Average Tips per Week:
The model provides a tip for every game it works on, so as of now that will be a tip for every Premier League game, with the expansion of the model increasing that number to a tip on every game from every league we work on.

As human emotion and the mind is taken out of the equation, I expect very low variance in my tips.

500 unit minimum is advised for following 1 league, with an additional 250 units for every extra league you follow. This is because if we have predictions for every game in the top 5 leagues, you could have 490 units staked at the same time.

I currently run with flat-stakes across all predictions (10 units), however, there will be work done to see if definitive patterns and trends can be spotted in the results, which could then influence our staking decisions.

Dates of activity:
The football never stops, and as such, neither will the tipping! We should have tips 12 months a year, tipping on the major leagues while they're on, and making do with the summer leagues when they're not, as the model doesn't care about the quality of football, only the quality of stats available!

Total profit
-4.070 units


Bets won
29/79 (36.709%)

Total stake

Avg. stake

Avg. odds

Decided tips

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