Break Stats -- Turning Stone Classic XXXI 9-Ball Open, January 2019

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Here are some aggregate break statistics from the Turning Stone Classic XXXI 9-Ball Open played January 10-13, 2019 at the Turning Stone Resort Casino in Verona, New York. Free live streaming was provided on the Facebook page of UpState AL.

This was a 128-player double-elimination event. Jayson Shaw won the tournament, defeating Shane Van Boening in the final match.

Conditions -- The conditions for the streamed matches in this event included:
- Diamond 9-foot table with pro-cut corner pockets;​
- Tournament Blue Simonis 860 cloth;​
- Aramith Tournament Pro-Cup TV balls with the measles cue ball;​
- Diamond plastic triangle rack;​
- winner breaks from a central box (2 diamonds wide);​
- loser racks, with the 1-ball on the foot spot;​
- cue-ball fouls only except during the act of shooting;​
- no jump cues allowed;​
- no shot clock;​
- all slop counts; and​
- lag for opening break.​

The stats are for the 23 matches (336 games) played on the main streaming table. These matches represented 9.1% of the event's total of 252 matches played (2 forfeits), and are listed here in the order in which they were played. [Note: These stats exclude the first game of the Michas/Andrade match, because only the end of that game was shown on the stream. So the tracked games number 335.]

Thurs., Jan. 10, 2019
Zion Zvi defeated Ron Casanzio 9-7, Billy Thorpe d. Rob Hart 9-2,
Loree Jon Hasson d. Qays Kolee 9-4, and Jeremy Sossei d. Mika Immonen 9-5.​
Fri., Jan. 11
Jason Michas d. John Andrade 9-8, Frankie Hernandez d. Martin Daigle 9-6,
Dale Kimmet d. Eric Croteau 9-5, Denis Grabe d. Shaun Wilkie 9-8,
Kyle Pepin d. Jorge Rodriguez 9-6, Zvi d. Johnny Archer 9-7, and Jennifer Barretta d. Hasson 9-8.​
Sat., Jan. 12
Thorsten Hohmann d. Pepin 9-2, Luc Salvas d. Wilkie 9-8,
Jayson Shaw d. Dennis Hatch 9-7, Danny Hewitt d. Hohmann 9-6,
Thorpe d. Eric Hjorleifson 9-2, Petri Makkonen d. Shaw 9-7, and Shaw d. John Morra 9-8​
Sun., Jan. 13
Shane Van Boening d. Makkonen 9-3, Shaw d. Tommy Tokoph 9-2,
Van Boening d. Sossei 9-3 (Hotseat match), Shaw d. Sossei 9-4 (Semifinal), and Shaw d. Van Boening 13-7(Finals).​

Overall results
Successful breaks (made at least one ball and did not foul) -- 64% (129 of 201) for match winners, 54% (73 of 134) for match losers, and 60% (202 of 335) in total

Breaker won the game -- 65% (130 of 201) for match winners, 40% (53 of 134) for match losers, and 55% (183 of 335) in total

Break-and-run games -- 32% (64 of 201) for match winners, 13% (18 of 135) for match losers, and 24% (82 of 335) in total​

Here's a breakdown of the 335 games (for match winners and losers combined).

Breaker made at least one ball and did not foul:​
Breaker won the game: 140 (42% of the 335 games)​
Breaker lost the game: 62 (19%)​
Breaker fouled on the break:​
Breaker won the game: 8 (2%)​
Breaker lost the game: 23 (7%)​
Breaker broke dry (without fouling):​
Breaker won the game: 35 (10%)​
Breaker lost the game: 67 (20%)​
Therefore, whereas the breaker won 55% (183 of 335) of all games,​
He won 69% (140 of 202) of the games in which he made at least one ball on the break and did not foul.​
He won 26% (8 of 31) of the games in which he fouled on the break.​
He won 34% (35 of 102) of the games in which he broke dry but did not foul.​
He won 32% (43 of 133) of the games in which he either fouled on the break or broke dry without fouling.​

Break-and-run games -- The 82 break-and-run games represented 24% of all 335 games, 45% of the 183 games won by the breaker, and 41% of the 202 games in which the break was successful (made a ball and didn't foul). The 82 break-and-run games (including 9's on the break) consisted of two 4-packs (both by Shaw), three 3-packs (2 by Thorpe and 1 by Grabe), eight 2-packs, and 49 singles. No one in the 23 streamed matches broke and ran more than 4 games in a row.

9-balls on the break -- The 82 break-and-run games included 4 9-balls on the break (1.2% of the 335 breaks).
 
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Miscellany from the data for the Turning Stone Classic XXXI 9-Ball Open
[This relates only to the 23 streamed matches, not to all matches in the event.]

• The most balls made on a single break was 3, done 15 times (including twice on fouled breaks).

• The average number of balls made on the break was 1.0 (this includes dry and fouled breaks). On successful breaks (made at least one ball and did not foul), the average was 1.5, and the distribution was 61% 1 ball, 33% 2 balls, and 6% 3 balls.

• 46% (155 of 335) of the games ended in one inning – 24% (82) won by the breaker (B&R) and 22% (73) won by the non-breaker. 11% (38 of 335) of the games lasted more than 3 innings.

• 39% (130 of 335) 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) – 41% (82 of 202)​
- By the non-breaker after fouls on the break – 52% (16 of 31)​
- By the non-breaker after dry breaks – 31% (32 of 102)​

• The player who made the first ball after the break:
- Won the game in that same inning 57% of the time (188 of 328)​
- Won the game in a later inning 20% of the time (67 of 328)​
- Lost the game 22% of the time (73 of 328)​
[Note -- total games used here are 328 rather than 335 to eliminate the 7 games in which no ball was made after the break (4 9-balls on the break and 3 games lost on 3 consecutive fouls.]​

• For the 22 races to 9 (i.e., excluding the finals race to 13) the loser won an average of 5.4 games. The loser won 3 or fewer games in 6 of those 22 matches. Five of those 22 matches went hill/hill.

• The average elapsed time for the 22 races to 9 was 78 minutes, or 5.4 minutes per game. The elapsed time for each match was measured from the lag until the winning ball was made (or conceded), so it includes time for racking and timeouts.

• The Barretta d. Hasson match was both longest in elapsed time, at 140 minutes for the 17 games, and highest in average minutes per game, at 8.2 min./game.

• The Thorpe d. Hjorleifson match was shortest in elapsed time, at 44 minutes for the 11 games, and tied with the Makkonen d. Shaw match (16 games) for lowest in average minutes per game, at 4.0 min./game.

• Breaking fouls averaged 1 for every 10.8 games, other fouls 1 for every 3.9 games, and missed shots about 1 for every 1.7 games.

• About 38% of the games involved one or more safeties.
 
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Do you by chance track individual player stats? If you do, it would be interesting to group them by FargoRate categories (ex increments 25 points). You would be able to get sense of player averages at the different pro levels. In particular, it would be interesting to see stuff like break success, BnR%, percentage of wins after making the first ball etc.
 
Event winner Shaw appeared in 6 of the 23 streamed matches (winning 5, losing 1). What did his stats look like compared with those of the other players?

Successful breaks (made at least one ball and did not foul):
• Shaw -- 70% (37 of 53)
• Match winners excluding Shaw's 5 wins -- 62% (96 of 155)
• Match losers excluding Shaw's 1 loss -- 54% (69 of 127)
• Total -- 60% (202 of 335)

Breaker won the game:
• Shaw -- 68% (36 of 53)
• Match winners excluding Shaw's 5 wins -- 63% (97 of 155)
• Match losers excluding Shaw's 1 loss -- 39% (50 of 127)
• Total -- 55% (183 of 335)

Break-and-run games, on all breaks:
• Shaw -- 42% (22 of 53)
• Match winners excluding Shaw's 5 wins -- 29% (45 of 155)
• Match losers excluding Shaw's 1 loss -- 12% (15 of 127)
• Total -- 24% (82 of 335)

Break-and-run games, on successful breaks:
• Shaw -- 59% (22 of 37)
• Match winners excluding Shaw's 5 wins -- 47% (45 of 96)
• Match losers excluding Shaw's 1 loss -- 22% (15 of 69)
• Total -- 41% (82 of 202)

Average minutes per game:
• Shaw's 6 matches -- 4.6
• The other 17 matches -- 5.7
• All 23 streamed matches -- 5.4
 
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Great work to say the least. Thanks for all of the time
and effort you put into this, I enjoyed looking at all of
the data.
 
Do you by chance track individual player stats? If you do, it would be interesting to group them by FargoRate categories (ex increments 25 points). You would be able to get sense of player averages at the different pro levels. In particular, it would be interesting to see stuff like break success, BnR%, percentage of wins after making the first ball etc.

Nice idea; yes, I could do that. But I'm reluctant to spend much time on cells with limited data. The 23 streamed matches for this event involved 30 different players (20 appeared once, 7 twice, 2 thrice, and 1 six times). I couldn't find FargoRates for 3 of the 30 players, but if we put the players in 25-point FargoRate groups starting from 550, the other 27 players appear in 9 different groups (using tonight's FargoRates), with more than 3 players in only 2 of those 9 groups (725-749 and 775-799). So I'm not sure whether I want to spend time on that; I'll give it some thought later.

[We could use multiple events to increase the cell counts, but .............. ugh]
 
10.8% foul rate on breaks is pretty surprising to me.

I would have bet the under on that.

BB -- the 10.8 number in post #2 is the number of games per breaking foul, calculated as 335 games divided by 31 breaking fouls. So the percentage of breaking fouls is the reciprocal of that, or 31 divided by 335 = 9.3% (or, equivalently, 1 divided by 10.8). But the 9.3% foul rate was, indeed, quite high this time.

The percentage of breaks that are fouled jumps around quite a bit from event to event because of the relatively low frequency of fouled breaks. For the streamed games I have watched in the last 14 Turning Stone events, this year's 9.3% foul rate is second highest, exceeded only by the 9.8% rate at TS XX in 2013. The lowest rate was 3.7% at TS XXVI, and the rate for all 14 events combined was 7.1%.
 
Nice idea; yes, I could do that. But I'm reluctant to spend much time on cells with limited data. The 23 streamed matches for this event involved 30 different players (20 appeared once, 7 twice, 2 thrice, and 1 six times). I couldn't find FargoRates for 3 of the 30 players, but if we put the players in 25-point FargoRate groups starting from 550, the other 27 players appear in 9 different groups (using tonight's FargoRates), with more than 3 players in only 2 of those 9 groups (725-749 and 775-799). So I'm not sure whether I want to spend time on that; I'll give it some thought later.

[We could use multiple events to increase the cell counts, but .............. ugh]

For sure, you might only get three players under 700 on the stream and they won't be representative of anything. I would probably limit the data to professionals or specifically 725 and above. The streams will likely favour 750+ players anyhow. It would be interesting to see a clear representation of how much better SVB, for example, is than mid tier pros. I expect the difference is fairly small, but even that small difference makes huge...umm difference lol.

Another solution is to keep the aggregate data on all levels going forward and within a couple years you will have a fairly strong representation of the standard across different playing levels.
 
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