2017 Memphis Open

The 2017 Memphis Open was a tennis tournament, played on indoor hard courts. It was the 42nd edition of the Memphis Open, and part of the ATP World Tour 250 series of the 2017 ATP World Tour. It took place at the Racquet Club of Memphis in Memphis, United States, from 13 through 19 February 2017.

2017 Memphis Open
Date13 February – 19 February
Edition42nd
Draw28S / 16D
Prize money$642,750
SurfaceHard
LocationMemphis, United States
Champions
Singles
Ryan Harrison
Doubles
Brian Baker / Nikola Mektić

Points and prize money

Point distribution
Event W F SF QF Round of 16 Round of 32 Q Q2 Q1
Singles 250 150 90 45 20 0 12 6 0
Doubles 0 N/A N/A N/A N/A

Prize money
Event W F SF QF Round of 16 Round of 32 Q2 Q1
Singles $114,595 $60,355 $32,695 $18,630 $10,975 $6,505 $2,925 $1,465
Doubles $34,810 $18,300 $9,920 $5,670 $3,320 N/A N/A N/A
Doubles prize money per team

Singles main draw entrants

Seeds
Country Player Rank1 Seed
 CRO Ivo Karlović 18 1
 USA John Isner 23 2
 USA Sam Querrey 27 3
 USA Steve Johnson 31 4
 AUS Bernard Tomic 33 5
 FRA Adrian Mannarino 56 6
 BEL Steve Darcis 59 7
 TPE Lu Yen-hsun 61 8
  • 1 Rankings as of February 6, 2017

Other entrants

The following players received wildcards into the main draw:

The following players received entry from the qualifying draw:

The following player received entry as a lucky loser:

Withdrawals
Before the tournament

Doubles main draw entrants

Seeds
Country Player Country Player Rank1 Seed
 PHI Treat Huey  BLR Max Mirnyi 51 1
 AUT Oliver Marach  FRA Fabrice Martin 68 2
 SWE Robert Lindstedt  NZL Michael Venus 78 3
 ESP Guillermo García López  IND Leander Paes 94 4
  • 1 Rankings are as of February 6, 2017.

Other entrants

The following pairs received wildcards into the main draw:

Withdrawals
Before the tournament
  • Guillermo García-López

Champions

Singles
Main article: 2017 Memphis Open – Singles

Doubles
Main article: 2017 Memphis Open – Doubles
gollark: You would then need giant datasets of Discord conversations and summaries, I think?
gollark: Good idea, but it does still need to know which bits to look at, which might be hard for a model mostly trained on people writing somewhat formally.
gollark: The datasets for summarization tend to be news and stuff so it might not transfer well.
gollark: This is technically possible, but probably hard.
gollark: I was considering harvesting some Google Colab™ time to train a similar neural network on text summarization.

References
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