TT Lossbeater/TT Cup
Stanislav Kafka vs Milan Flesar 2-3 · TT Cup
TT Cup
Played Oct 5Finished
Stanislav Kafka
Home
1.57
At publish
Won last 2Form at publish
4–6Last 10Won last 2
Final
2 – 3
Set by set
611119117411911
Form at publish
5–5Last 10Lost last 1
The read, and what happenedsealed at 19:54 and never touched: 58.9% is still 58.9%
Graded at full timeStanislav Kafka to win
58.9%
58.9%
Sealed before the first serve and never touched. The record is only checkable because of that.
Read
Wrongnot sent
How it is goingStanislav Kafka above · Milan Flesar below
Set 1 · 6-11
Set 2 · 11-9
Set 3 · 11-7
Set 4 · 4-11
Set 5 · 9-11
Point 90 set 5, 9-11 · our read held Stanislav Kafka at 58.9%
90 points played in total.
H2H Stanislav Kafka 2–1 Milan Flesar Milan Flesar has won 1 of their 3 meetings.
The pickNothing sent
Sealed 19:5458.9%Our read63.7%to break even at 1.571
short by 4.8pp63.7% (what it has to beat, and what the price implies: at 1.571 they are the same number)
What took a side2 for · 0 against · 4 silent
SignalWhat it readSideVerdictWhy it voted that way
Market
1.57 (59%)
Kafka
Wrong
no bar to clear: a priced side always votes
H2H
2-1 in 3
—
Too close
gap of 1, under the gap of 2
Rating (G2)
Kafka 1593 vs Flesar 1521
Kafka
Wrong
both rated, 72 points apart, no bar to clear
Form (L5)
Kafka 60% vs Flesar 60%
—
Too close
0pp gap, under the 15pp bar
Form (L10)
Kafka 40% vs Flesar 50%
—
Too close
10pp gap, under the 15pp bar
Streak
Kafka +2 · Flesar -1
—
Too close
neither holds a run of 3 on their own
Not whether we were right, but which read failed. One match proves nothing: the calibration is the aggregate of rows like these, and that aggregate does not exist yet.
This table lists the interface catalogue: the one you already see in ALIGN / CONFLICT. The engine weighs its own subset, and they are not the same set. It says so here instead of hiding it.
2 for, 4 silent, and still no pick. The gate cuts it all the same.
Signal confidence: 59% · Slight lean (leans Kafka) · sealed before the serve
Decision Summary
Why this prediction
- Flesar form (60%) outperforms what odds imply (44%)
Market vs Signals
Market oddsKafka 1.571 / Flesar 2.25
Signal prob.59% / 41%
Edge (Kafka)-4.8pp (anti-value)
TT Cup · #13247952
Kafka1.57
40%won 4 of 10 · last 10
Last match: today
Won last 2
oldest → newest40%takes the first set · 4 of 10
As favourite54% won 7 of 13
Up 2–0, closes it80% 4 of 5
Up 1–0, closes it80% 4 of 5
Flesar2.25
50%won 5 of 10 · last 10
Last match: today
oldest → newest
50%takes the first set · 5 of 10
As underdog43% won 62 of 145
Down 0–1, comes back21% 18 of 88
Down 0–2, comes back7% 4 of 62
Head to head
H2H window
The recordacross 3 meetings
HomeKafka2wins
AwayFlesar1wins
Kafka leads 2–1.
First set to match win · in this matchup
Not enough first sets
Neither side has won the first set three times here, so there is no rate to publish.
Set by set, in these 3 meetingsevery set carries its own n: a fifth set only exists when the match reaches one
S1Kafka2Flesar1n = 3Kafka took 2 of 3
S2Kafka2Flesar1n = 3Kafka took 2 of 3
S3Kafka2Flesar1n = 3Kafka took 2 of 3
How those 3 finishedcounts, and the six add up to 3. A scoreline is a name, not a code
3–0Straight, home267%
3–1Four sets, home00%
3–2Decider, home00%
2–3Decider, away00%
1–3Four sets, away00%
0–3Straight, away133%
These 3 meetings
MetricKafkaFlesarK − FBasisWins21+1in 3 meetingsFirst set21+1of 3Avg pointsper match30.028.7+1.3per matchSweeps21+13–0 or 0–3
Career, not this matchup
MetricKafkaFlesarBasisWin rate · lifetime68 / 111 vs 241 / 46361.3%52.1%68 / 111 vs 241 / 463Avg sets wonper match2.22.0per matchAvg pointsper match37.536.7per match
Deep Dive by Market
Each market builds on the overview aboveForecast
History
Forecastthe model predicts this marketHistorythis is what the H2H did: no prediction
Match Winner
KafkaKafka 1.57/Flesar 2.25
Read onlywould have missed
What we said
58.9%
Kafka · sealed before the serve
What happened
2–3
Flesar won
Recent Results
H2H · no odds on record3
WSep 27 · 21:03S. Kafka341211113M. Flesar2510780
LSep 27 · 18:05S. Kafka1954100M. Flesar341111123
WSep 10 · 20:05S. Kafka371111153M. Flesar2795130
S. Kafka10
WOct 5 · 19:05S. Kafka1.831111113D. Hons1.836980WOct 5 · 18:05S. Kafka1.80711811113J. Martin1.9111911352LSep 29 · 17:05T. Beran1.531211511113S. Kafka2.3814411832WSep 29 · 16:35J. Ruzicka1.7378110S. Kafka2.001111133LSep 29 · 15:35S. Kafka2.50711781T. Trnka1.5011911113LSep 29 · 14:05T. Beran1.671113113S. Kafka2.1041160WSep 27 · 21:03M. Flesar1.7310780S. Kafka2.001211113LSep 27 · 20:05S. Kafka1.918810P. Zivny1.801111113LSep 27 · 19:05S. Kafka1.806670D. Hons1.911111113LSep 27 · 18:05S. Kafka1.4454100M. Flesar2.631111123
M. Flesar10
LOct 5 · 19:35J. Martin1.61111111113M. Flesar2.20135671WOct 5 · 18:35M. Flesar2.10612119113D. Hons1.67111041172WOct 4 · 21:33L. Vosyka1.5094120M. Flesar2.501111143LOct 4 · 20:05L. Vosyka1.571113113M. Flesar2.2591190WOct 4 · 19:35J. Martin1.50941171M. Flesar2.5011115113WOct 4 · 18:35M. Flesar1.731116113J. Kabelka2.0071470WOct 4 · 17:03J. Ruzicka1.33611111552M. Flesar3.251113613113LOct 4 · 16:35M. Flesar1.535780M. Chmelicek2.381111113LSep 27 · 21:03M. Flesar1.7310780S. Kafka2.001211113WSep 27 · 18:05S. Kafka1.4454100M. Flesar2.631111123
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