Break Stats -- 2022 Predator World 10-Ball Championship, March/April 2022

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Here are some aggregate break statistics from the 2022 Predator World 10-Ball Championship played March 28 - April 1, 2022 at the Rio All-Suite Hotel & Casino in Las Vegas, Nevada with free streaming on Billiard TV and on YouTube. The primary commentators were George Teyechea, Mark White, and Tony Robles. Wojciech Szewczyk won the event, defeating Cristopher Tevez in the final match.

This was an invitational 128-player event with double-elimination down to 32 players (16 on the winners' side and 16 on the one-loss side). The 16 players (out of the final 32) with the highest WPA rankings were then seeded into the final bracket and the positions of the other 16 were drawn randomly. The matches were races to 8 in the double-elimination portion and races to 10 thereafter. The stats are for all 25 matches streamed on the arena table. These 25 matches represented 10.5% of the total of 239 matches played in the event.

The conditions for the streamed matches included:
- Predator Apex 9-foot table with 4¼ corner pockets;​
- Predator cloth (blue, new);​
- Predator Arcos II balls, including a black-triangles cue ball;​
- Predator Arena lights;​
- referee racks using a Predator triangle rack, with the 1-ball on the spot (2-ball and 3-ball need not be on the back corners);​
- alternate breaks from anywhere behind the head string;​
- call shots (but not safes), with the opponent having a choice of shooting or passing it back after a ball is pocketed illegally;​
- spot any 10-ball made on the break;​
- early combinations or caroms on the 10-ball are not game wins; spot the 10-ball and continue shooting;​
- jump cues allowed;​
- foul on all balls;​
- 3-foul rule in effect (occurred once);​
- 30-second shot clock (60 seconds after the break) with one 30-second extension allowed per player per game; and​
- lag for opening break.​

The 25 matches (334 games) tracked were as follows, shown in the order in which they were played. The stats are for 332 games rather than 334 because 2 of the games in Match 18 were awarded to Kaçi as a penalty assessed against Pagulayan for being late to the match.

Mon. March28, 2022
1. Eklent Kaçi defeated Emil Gangflot 8-3​
2. Skyler Woodward d. Niels Feijen 8-6​
3. Thorsten Hohmann d. Ping-Han Ko 8-6​
4. Francisco Sanchez-Ruiz d. Jeremy Seaman 8-5​
5. Lee Vann Corteza d. Albin Ouschan 8-4​

Tues. March 29
6. Sanjin Pehlivanovic d. Ping-Chung Ko 8-7​
7. Shane Van Boening d. Jason Theron 8-0​
8. Oscar Dominguez d. Jose Parica 8-6​
9. Jesus Atencio d. Hiroyuki Shozaki 8-0​
10. Kun-Lin Wu d. Riku Romppanen 8-2​

Wed. March 30
11. Omar Al-Shaheen d. Roberto Gomez 8-7​
12. Corteza d. Pin-Yi Ko 8-4​
13. Darren Appleton d. John Schmidt 8-4​
14. Eric Roberts d. Yukio Akagariyama 8-7​
15. Al-Shaheen d. P-C Ko 8-7​

Thurs. March 31
16. Francisco Sanchez-Ruiz d. Alex Kazakis 8-7​
17. Jayson Shaw d. Hohmann 8-0​
18. Kaçi d. Alex Pagulayan 10-3​
19. Shaw d. Jonas Souto 10-3​
20. Appleton d. Mika Immonen 10-5​

Fri. April 1
21. Naoyuki Oi. d. Chris Reinhold 10-5 (Round of 16)​
22. Shaw d. Pehlivanovic 10-4 (Quarterfinal)​
23. Wojciech Szewczyk d. Kaçi 10-9 (Semifinal)​
24. Cristopher Tevez d. Shaw 10-6 (Semifinal)​
25. Szewczyk d. Tevez 10-8 (Final)​

Overall results

Successful breaks (made at least one ball and did not foul) -- 70% (119 of 170) for match winners, 56% (91 of 162) for match losers, and 63% (210 of 332) in total​
Breaker won the game -- 66% (113 of 170) for match winners, 38% (61 of 162) for match losers, and 52% (174 of 332) in total​
Break-and-run games on all breaks -- 24% (40 of 170) for match winners, 14% (22 of 162) for match losers, and 19% (62 of 332) in total​
Break-and-run games on successful breaks -- 34% (40 of 119) for match winners, 24% (22 of 91) for match losers, and 30% (62 of 210) in total​

Here's a breakdown of the 332 games (for match winners and losers combined).

Breaker made at least one ball and did not foul:
Breaker won the game: 130 (39% of the 332 games)​
Breaker lost the game: 80 (24%)​

Breaker fouled on the break:
Breaker won the game: 9 (3%)​
Breaker lost the game: 14 (4%)​

Breaker broke dry (without fouling):
Breaker won the game: 35 (11%)​
Breaker lost the game: 64 (19%)​

Therefore, whereas the breaker won 52% (174 of 332) of all games,
He won 62% (130 of 210) of the games in which the break was successful (made at least one ball and did not foul).​
He won 36% (44 of 122) of the games in which the break was unsuccessful (fouled or dry).​

Break-and-run games -- The 62 break-and-run games represented 19% of all 332 games, 36% of the 174 games won by the breaker, and 30% of the 210 games in which the break was successful (made a ball and didn't foul).

With alternating breaks, B&R "packages" of the normal type are not possible. But we can still look at the breaks of a given player and see how many he ran on his own successive breaks, and we can call these "alternate-break packages." The 62 break-and-run games consisted of 6 alternate-break 2-packs (1 each by Dominguez, Akagariyama, Sanchez-Ruiz, Shaw, Appleton, and Szewczyk) and 50 singles. No one in these 25 matches broke and ran more than 2 games in a row on his own breaks.

10-Balls on the break -- Six 10-balls were made on the break (1.8% of all 332 breaks). They were spotted.
 
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Miscellany from the data for the 2022 Predator World 10-Ball Championship:
[This relates only to the 25 streamed matches, not to all matches in the event.]

• The most balls made on a single break was 4, done three times -- once by Feijen (lost the game), once by Tevez (won the game by B&R), and once by Immonen (also fouled on that break but won the game).

• The average number of balls made on the break was 0.9 (this includes dry and fouled breaks). On successful breaks (made at least one ball and did not foul), the average was 1.4.

• 38% (126 of 332) of the games ended in one inning – 19% (62) won by the breaker (B&R) and 19% (64) won by the non-breaker. Seventeen percent (55 of 332) of the games lasted more than 3 innings. The 3 games with the most innings ended on the non-breaker's 7th visit to the table.

• 27% (90 of 332) of the games were run out by the player who was at the table following the break. These run-outs were:
By the breaker after successful breaks (B&R games) – 30% (62 of 210);​
By the non-breaker after fouls on the break – 35% (8 of 23); and​
By the non-breaker after dry breaks – 20% (20 of 99).​

• The player who made the first ball after the break:
Won the game in that same inning 44% of the time (147 of 332);​
Won the game in a later inning 26% of the time (86 of 332); and​
Lost the game 30% of the time (99 of 332).​

• The loser won an average of 4.4 games in the 17 races to 8 and 5.4 games in the 8 races to 10. Six matches went to hill/hill; the most lopsided matches in the two stages were three at 8-0 and two at 10-3.

• The average elapsed time for the 12 races to 8 was about 86 minutes, or 7.0 minutes per game. The average elapsed time for the 8 races to 10 was about 97 minutes, or 6.3 minutes per game. The elapsed time was measured from the lag until the winning ball was made (or conceded), so it includes time for racking and commercial breaks, which were numerous in these matches.

• The race to 10 that was both longest in elapsed time, at 141 minutes, and highest in average minutes per game, at 7.4, was Szewczyk d. Kaçi 10-9. The race to 8 that was longest in elapsed time, at 118 minutes, was Pehlivanovic d. P-C Ko 8-7. The race to 8 highest in average minutes per game, at 8.0, was Kaçi d. Gangflot 8-3.

• The race to 10 that was both shortest in elapsed time, at 70 minutes, and lowest in average minutes per game, at 5.4, was Shaw d. Souto 10-3. The race to 8 that was shortest in elapsed time, at about 49 minutes, was Shaw d. Hohmann 8-0. The race to 8 lowest in average minutes per game, at 5.7, was Wu d. Romppanen 8-2.

• Breaking fouls averaged 1 for every 14.4 games, other fouls 1 for every 3.4 games, and missed shots about 1 for every 1.3 games.

• One or more safeties were played in about 46% of all games and 56% of games that were not B&Rs.
 
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Miscellany from the data for the 2022 Predator World 10-Ball Championship:
[This relates only to the 25 streamed matches, not to all matches in the event.]

• The most balls made on a single break was 4, done twice -- once by Feijen, who lost the game, and once by Tevez, who won the game by B&R.

i think kaci made 5, plus the cue ball..
 
i think kaci made 5, plus the cue ball..
In the arena matches, Kaçi appeared 3 times, did not foul on the break in any of those games, and made at most 3 balls on any break.

But I checked my notes on all 23 of the fouled breaks in the 25 matches in the arena. Immonen made 4 object balls on a fouled break in Game 12 against Appleton, so I edited post #2 above to include that. Perhaps that is the break you are remembering, or it could have been in a match I did not watch on one of the outside tables.
 
So B&R about 10% higher than Las Vegas Open. I suspect that is cos they had stronger players on TV table than Las Vegas Open :LOL:

w10b 22.JPG
 
In the arena matches, Kaçi appeared 3 times, did not foul on the break in any of those games, and made at most 3 balls on any break.

But I checked my notes on all 23 of the fouled breaks in the 25 matches in the arena. Immonen made 4 object balls on a fouled break in Game 12 against Appleton, so I edited post #2 above to include that. Perhaps that is the break you are remembering, or it could have been in a match I did not watch on one of the outside tables.

yes, that was it. lol
 
Thank you for posting these statistics , I feel it would be interesting to learn what the average cue ball speed was during the break in correlation to the number of balls made on the break .
Similar to what they do during a professional bowling tournament or event at the TV table .
 
Biggest match upsets, ranked by difference in Fargo rating (hopefully no data entry errors here):

141 points: Nils Johanning (667) def. Wiktor Zielinski (808), 8-7
134 points: Eric Roberts (678) def. Niels Feijen (812), 8-4
109 points: Pedro Botta (658) def. Danny Hewitt (767), 8-0
97 points: Nils Johanning (667) def. Jan van Lierop (764), 8-1
97 points: Eric Roberts (678) def. Yukio Akagariyama (775), 8-7
86 points: Eric Roberts (678) def. Roman Hybler (764), 8-5
83 points: Blaine Barcus (715) def. Kun-Lin Wu (798), 8-7
80 points: Eric Roberts (678) def. Max Eberle (758), 8-4
69 points: Sharik Sayed (751) def. Pin-Yi Ko (820), 8-7
65 points: Donny Mills (749) def. Aloysius Yapp (814), 8-3

Quite a week for Eric Roberts, assuming this is the correct Eric Roberts.
 
Top within-tournament Fargo ratings, assuming opponents played to their normal speed (sum all wins, sum all losses, calculate predicted rating against mean of opponents' ratings weighted by number of games played against):

Christopher Tevez (875): 72 W-42 L against avg. 797
Jayson Shaw (858): 67 W-38 L against avg. 776
Eklent Kaci (850): 63 W-40 L against avg. 785
Carlo Biado (845): 42 W-23 L against avg. 759
Lee Van Corteza (841): 40 W-29 L against avg. 795
Pijus Labutis (840): 33 W-22 L against avg. 782
Ping-Chung Ko (838): 30 W-18 L against avg. 765
Robbie Capito (834): 43 W-29 L against avg. 778
Naoyuki Oi (833): 53 W-30 L against avg. 751
Wojciech Szewczyk (829): 74 W-51 L against avg. 775

Note the eventual winner Szewczyk only had the 10th best in-event rating!


Best performances relative to their Fargo ratings:

129 points: Pedro Botta (658 rating vs. 787 performance)
114 points: Christopher Tevez (761 rating vs. 875 performance)
110 points: Eric Roberts (678 rating vs. 788 performance)
95 points: Nils Johanning (667 rating vs. 762 performance)
74 points: Florijan Maric (605 rating vs. 679 performance)
65 points: Pijus Labutis (775 rating vs. 840 performance)
55 points: Robbie Capito (779 rating vs. 834 performance)
53 points: Riley Adkins (641 rating vs. 694 performance)
52 points: Julio Burgos (728 rating vs. 780 performance)
50 points: Chris Reinhold (743 rating vs. 793 performance)


Games involving the two players with no Fargo ratings were omitted (Kento Oda, Kyle Akaloo)
 
Top within-tournament Fargo ratings, assuming opponents played to their normal speed (sum all wins, sum all losses, calculate predicted rating against mean of opponents' ratings weighted by number of games played against):

Christopher Tevez (875): 72 W-42 L against avg. 797
Jayson Shaw (858): 67 W-38 L against avg. 776
Eklent Kaci (850): 63 W-40 L against avg. 785
Carlo Biado (845): 42 W-23 L against avg. 759
Lee Van Corteza (841): 40 W-29 L against avg. 795
Pijus Labutis (840): 33 W-22 L against avg. 782
Ping-Chung Ko (838): 30 W-18 L against avg. 765
Robbie Capito (834): 43 W-29 L against avg. 778
Naoyuki Oi (833): 53 W-30 L against avg. 751
Wojciech Szewczyk (829): 74 W-51 L against avg. 775

Note the eventual winner Szewczyk only had the 10th best in-event rating!


Best performances relative to their Fargo ratings:

129 points: Pedro Botta (658 rating vs. 787 performance)
114 points: Christopher Tevez (761 rating vs. 875 performance)
110 points: Eric Roberts (678 rating vs. 788 performance)
95 points: Nils Johanning (667 rating vs. 762 performance)
74 points: Florijan Maric (605 rating vs. 679 performance)
65 points: Pijus Labutis (775 rating vs. 840 performance)
55 points: Robbie Capito (779 rating vs. 834 performance)
53 points: Riley Adkins (641 rating vs. 694 performance)
52 points: Julio Burgos (728 rating vs. 780 performance)
50 points: Chris Reinhold (743 rating vs. 793 performance)


Games involving the two players with no Fargo ratings were omitted (Kento Oda, Kyle Akaloo)
skip100 -- is the method you used for these calculations the same as Mike Page uses when he posts within-tournament ratings? If not, have you compared your method against his for any such ratings he has posted?
 
skip100 -- is the method you used for these calculations the same as Mike Page uses when he posts within-tournament ratings? If not, have you compared your method against his for any such ratings he has posted?
If not precisely the same, the difference would only be a couple points here or there.

I'm using the following formula, where o_p is the opponent's win percentage for the match and o_rating is the opponent's rating:
144 * ln((1 - o_p) / o_p) + o_rating

As an example, in an 8-4 win against a player rated 600, o_p is 1/3 and we get 144 * ln(2) + 600 = 100 + 600 = 700. This matches up with the Fargo setup where 100 points indicates a 2-to-1 edge for the higher-rated player.

The numbers above come from aggregating across all the player's matches, summing up their wins and losses and then using an average of the opponent's ratings weighted by the number of games involved. I've gone through examples before of how this can result in problems - playing two 500s and one 800 is not the same thing as playing three 600s - but in practice they are not really worth worrying about.

Note that this setup results in an undefined score when one of the players has zero wins. You can't take the log of 0 if o_p is 1 and you can't divide by 0 if o_p is 0.

I'd rather run the formula for each match separately and then take the weighted average of the resulting ratings, but every 0-win match would create an undefined score for both players involved (~10% of the players in this event).
 
If not precisely the same, the difference would only be a couple points here or there.

I'm using the following formula, where o_p is the opponent's win percentage for the match and o_rating is the opponent's rating:
144 * ln((1 - o_p) / o_p) + o_rating

As an example, in an 8-4 win against a player rated 600, o_p is 1/3 and we get 144 * ln(2) + 600 = 100 + 600 = 700. This matches up with the Fargo setup where 100 points indicates a 2-to-1 edge for the higher-rated player.

The numbers above come from aggregating across all the player's matches, summing up their wins and losses and then using an average of the opponent's ratings weighted by the number of games involved. I've gone through examples before of how this can result in problems - playing two 500s and one 800 is not the same thing as playing three 600s - but in practice they are not really worth worrying about.

Note that this setup results in an undefined score when one of the players has zero wins. You can't take the log of 0 if o_p is 1 and you can't divide by 0 if o_p is 0.

I'd rather run the formula for each match separately and then take the weighted average of the resulting ratings, but every 0-win match would create an undefined score for both players involved (~10% of the players in this event).
Thanks for the explanation.

[Looks like your formula is missing a right parenthesis.]
 
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