ATP Ranking Predictions for August 2018

We use our machine learning model to predict ATP rankings

In our last blog post, we detailed our new machine learning forecasting app that predicts future rankings. The app updates automatically on a weekly cadence and we’ve pulled the most recent results into 2 tables below.

The current top 10 ATP players are forecasted to remain stagnant into August. This is after Zverev defended his title at the Citi Open and last week’s tournaments have been accounted for.

Name July Rank Aug. Rank Pred. July Pts. Aug. Pt. Pred. Rank Diff.
Rafael Nadal 1 1 9175 9248 0
Roger Federer 2 2 7490 7167 0
Alexander Zverev 3 3 5688 5785 0
Juan Martin del Potro 4 4 5316 5457 0
Kevin Anderson 5.8 5 4400 4650 -0.8
Grigor Dimitrov 6 6 4652 4462 0
Marin Cilic 6.5 7 4194 4000 0.5
Dominic Thiem 8 8 3708 3899 0
John Isner 9 9 3436 3561 0
David Goffin 10.5 11 3118 3074 0.5

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Future Insights: Monthly ATP Ranking Forecast

How recent ATP player trends impact future rankings before they happen, updated live

Welcome back tennis fans… and no, we didn’t forget about your thirst for tennis and data analytics! Our new web app allows you to easily see a player’s ranking history, similar to the ATP site, but with an added ranking forecast, packaged as an interactive visualization not available anywhere else!*

Check out the app here (15 second load time**):

AppFull

More info below. Continue reading “Future Insights: Monthly ATP Ranking Forecast”

2017 Indian Wells Forecasts

Blogging from the bush, we’re bringing you our latest forecasts

We are on site in the California desert, bringing you our predictions for the 2017 Indian Wells BNP Paribas Open.

Updated 3/19/2017 1:45 pm ET

Final

Winning Player (rank) Losing Player (rank) W-L %
Roger Federer (10) Stanislas Wawrinka (3) 19-3 72%

Updated 3/18/2017 2 am ET

SF

Winning Player (rank) Losing Player (rank) W-L %
Stanislas Wawrinka (3) Pablo Carreno Busta (23) 2-0 76%
Roger Federer (10) Jack Sock (18) 2-0 83%

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2017 Australian Open Predictions

We use our predictive model to forecast unplayed tennis matches

Update 1/27/2017 6:05 pm

Final Predictions

Winning Player Losing Player W-L %
Roger Federer (17) Rafael Nadal (9) 11-23 73%

SF Results

2/2 matches correct

Update 1/25/17 6:45 pm

SF Predictions

Winning Player (rank) Lossing Player (rank) W-L %
Roger Federer (17) Stanislas Wawrinka (4) 18-3 83%
Rafael Nadal (9) Grigor Dimitrov (15) 7-1 62%

QF Results

2/4 matches correct.

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Predicting the Winner: 2016 Australian Open

Creating an algorithm for tennis match forecasting

So far, we’ve covered general descriptive statistics, such as yearly attribute trends, points won distributions, and competitiveness on our blog. We build on those findings by creating a model capable of predicting ATP tennis match winners. Using historical data points, we achieve 81% accuracy in predicting match winners for the 2016 Australian Open. We delve into the development process and share our predictions below.

Continue reading “Predicting the Winner: 2016 Australian Open”