From 1.06 to 1.85: Nguyen Nhu Le, Nhat Hoa and the Statistical Reversal at HBSF Tour 2

On the 27th inning of the carom 3-cushion final, Nguyen Nhu Le had 40 points in hand and needed exactly 10 more to reach 50. He stood behind the table, hands on the cue, and in a tournament where every figure had already been recorded, this moment was the only piece of data that could never be reproduced a second time. Ten points. One run. The score closed at 50-37, and on the other side of the table, Nguyen Nhat Hoa — known domestically by the nickname 'the bonus hunter' — had led 13-5 early, led 27-21 at the midpoint, then allowed the tie at 34-34 before collapsing in silence.

But the most notable figure from that afternoon was not 50-37. It was 1.06. That was Nhu Le's average in the semifinal, when he needed 47 innings to score 50 against Nguyen Van Tung Em — the slowest figure of all four semifinalists. And then, just one session later, he entered the final with an average of 1.85, the highest of any match recorded at HBSF Tour 2 MIN Table Cup 2026.

I sat in front of a screen in London, logging every inning into a simple spreadsheet, and I had to reread it three times to make sure I had not entered it wrong. A player averaging 1.06 in the semifinal does not usually win. A player averaging 1.06 in the semifinal even less usually averages 1.85 in the final. That is the starting point for everything I will write below.

The trophy is not on the scoreboard; it is in the average table. And the average table, at HBSF Tour 2, is telling a story the scoreboard conceals.

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Context: a two-discipline event, an editor, and a nickname

HBSF Tour 2 MIN Table Cup 2026 ran for six days, organised by the Ho Chi Minh City Billiards & Snooker Federation. This is a local federation tour — semi-professional, not tied to any UMB or international ranking system. The event runs two disciplines in parallel: carom 3-cushion (pocketless, scoring by inning, target 50 points) and American pool (pocket-based, set format, race-to-4 per set plus an overtime set).

One could call it a 'Vietnamese Billiards World Cup' in a headline. But a data journalist reads the figure before reading the name. The carom champion's prize was 70 million VND, roughly 2,700 US dollars. The pool champion's prize was 60 million VND, roughly 2,350 US dollars. Prize money scaled down to the top 16, with 30 million for runner-up, 10 million for joint bronze, 5 million for the carom Best Game and Outstanding Series, and 2 million for top 16.

In carom, a player competing at the UMB World Cup level typically earns one to two orders of magnitude more. 2,700 US dollars is a domestic tour cheque. That does not diminish the value of the title. It simply locates the tier of the event, and that is what I need before analysing anything else.

The prize structure here is unusually flat at the bottom — paying down to top 16 means the prize fund exists to encourage presence, not just to reward the winner. For a domestic tour trying to sustain a wide player base, this is an economically sensible choice. But it is also a signal that we are on a semi-professional stage, where the skill gap between players is much narrower than at an international event.

The season has at least two stops: Tour 2 here, and the HBSF Final Tour 2026 — the season closer — scheduled for December 16 to 20, 2026, at Rach Mieu Sports Hall in Ho Chi Minh City. That is why I am tracking this event not as a standalone match, but as a data point in a season series. A tournament is essentially a regression model, but everyone keeps calling it a race. And every regression model is only as good as its sample.

I need to state this clearly before going further: this article analyses two finals that took place in the same report but belong to two technically unrelated disciplines. Carom 3-cushion has no pockets, no break shot, no rack-luck factor. Pool has pockets, a rack, a break, and a set structure. Using the same metric to evaluate both finals is a methodological error. I separate them, and I recommend the reader do the same.

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Core: decoding the carom final

The final in raw numbers

Nguyen Nhu Le beat Nguyen Nhat Hoa 50-37 in 27 innings. Nhu Le's average: 1.85 points per inning. Nhat Hoa's average: 1.37 points per inning.

In the semifinal, Nhat Hoa beat Chiem Hong Thai 50-44 in 33 innings, an average of 1.52. Nhu Le beat Nguyen Van Tung Em 50-44 in 47 innings, an average of 1.06.

These three numbers — 1.06, 1.52, 1.85 — are the entire statistical story of the event. Put them side by side:

• Nhu Le semifinal: 1.06
• Nhat Hoa semifinal: 1.52
• Nhu Le final: 1.85
• Nhat Hoa final: 1.37

In the semifinal, Nhat Hoa scored 43 percent faster per inning than Nhu Le. In the final, Nhu Le scored 35 percent faster than Nhat Hoa. The two players swapped statistical positions almost completely within a single session. That is what I call a 'slope reversal'.

Why the reversal matters

Carom 3-cushion is one of the few sports where inning data can be recorded almost perfectly. Every point corresponds to an inning, every inning can be counted. There is no collective defence, no teammate screening, no randomness from the crowd. Only the player, the table, and the balls.

Therefore, carom average is closer to a structural indicator than almost any football metric. It is not xG — it is outcome that has already occurred. But it has a characteristic I always keep in mind: it is heavily influenced by the opponent. A player averaging 1.85 might be playing well, or might be facing an opponent who gives him space. A player averaging 1.06 might be playing poorly, or might be facing an opponent who locks him into a defensive battle.

This is why I do not rush to conclusion. Before saying Nhu Le 'transformed' in the final, I must rule out two other hypotheses:

Hypothesis one: Nhu Le benefited from opponent luck. Nguyen Van Tung Em in the semifinal is a strong defensive player, extending the match and lowering average for both sides. Nhat Hoa in the final is a more attacking player, shortening the match and raising average for both sides.

Hypothesis two: Nhu Le genuinely elevated in the big match. He played the final with a different mental state, selected different shots, and converted opportunities better when the opponent made errors.

These two hypotheses have entirely different consequences. If it is hypothesis one, Nhu Le's victory reflects mostly the nature of the table and the opponent. If it is hypothesis two, it is a signal of individual potential. My sample — one match — is far too small to distinguish them. But I have one more piece of data.

The closing sequence: the 10-point run

The most interesting part is not the mean average. It is the timing distribution.

Nhat Hoa led 13-5 early. He led 27-21 at the midpoint. By the time Nhu Le tied at 34-34, the match was level. And from 34-34, Nhu Le scored 16 points, closing with a run of 10 consecutive points, to end the match at 50-37.

Read that sequence again:

• Nhat Hoa leads 13-5
• Nhat Hoa leads 27-21
• Tie at 34-34
• Nhu Le runs 10, final score 50-37

This is not a match Nhu Le won from the start. This is a match where he trailed for most of the duration, tied at the back end, then exploded in the decisive five innings.

From a data perspective, this is the mark of a 'closer' profile — a player strong in the endgame. But caution is needed. A 10-point run at carom-to-50 has a specific technical property: it does not require the player to score 10 points in 10 innings from difficult positions. It only requires the player to hold the table position and convert one or two opponent errors. At a target of 50, a 10-point run is about 20 percent of the match total. At carom-to-40 (a more common international format), a 10-point run is 25 percent. At carom-to-30, it is 33 percent. Shorter formats amplify the weight of a single run. At target 50, the 10-point run is still a large run, but it is not a structurally overwhelming one.

This matters because it affects how I read Nhu Le's win. He won through a strong closing sequence. But that sequence occurred in a format where a single large run can reverse a match faster than in a long format. This is a factor that must be accounted for when comparing to international matches.

Nhat Hoa's profile: the front-runner who finishes second

On the other side, Nhat Hoa leaves a different statistical trace. He won the semifinal with an average of 1.52 — the second highest of the entire event. He led the final 13-5 and 27-21. He managed to tie at 34-34 after Nhu Le closed the gap. But from 34-34 onward, he scored almost nothing of note.

This is a 'front-running' profile — a player strong when leading, weak when level. I do not dare claim this from one match. But I have one more piece of data: the nickname itself.

'The bonus hunter' is how the domestic scene labels a player who regularly reaches the money rounds. This is not a nickname about elite talent — it is a nickname about consistency. A 'bonus hunter' is a player you can predict will go deep in a tournament, not necessarily the player you predict will win it.

Nhat Hoa's runner-up finish at Tour 2 fits this profile perfectly. He reached the final, he earned prize money, and he lost at the decisive moment. That is the mark of a player with a high floor but an unclear ceiling. In any sports model, this is the type of player of high value to a team or tour but difficult to predict in the final round.

I wonder whether Nhat Hoa is a case where we need more data to evaluate. With a reset, there is no way to know whether this profile is intrinsic or just a short data sequence. This is exactly the kind of situation the 'data limitations' section at the end of this article will address.

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Core (continued): decoding the pool final

Result and format structure

The pool final saw Nguyen Hoang Minh Tai defeat Lim Leng. This was a set-format match. The documentation I have records:

• Set 1: Lim Leng wins 4-2
• Set 2: Minh Tai wins 4-1
• Overtime set: Minh Tai wins 2-0

With this result, the interpretation depends on the overtime rule. If the overtime set is treated as an independent deciding set, the overall result is Minh Tai winning 2-1 in sets. If the overtime set is treated as part of set 2 (for example, a tiebreak within a set), the overall result could be 4-3. The documentation does not specify. This is one of the points I marked as 'low confidence' and will not draw conclusions from.

But one thing I can say is this: the match followed a 'momentum-swing' pattern. Lim Leng won the opening set. Minh Tai counter-attacked to win set two. The match went to an overtime set. This was not a one-sided match. It was a match where the momentum shifted at least twice.

Minh Tai's profile: the most credentialed player of the event

Here, we need to address what the original report omitted.

Nguyen Hoang Minh Tai is not a new figure. He is a former national champion, and HBSF Tour 2 was his third HBSF Tour title. Within the same event, Nguyen Nhu Le won the first HBSF Tour title of his career.

This creates an editorial paradox. The most-covered player in the original report was Nhu Le — the first-time champion. The least-covered was Minh Tai — the three-time champion. The editor chose the 'first title' story, not the 'veteran winner' story.

From a data standpoint, this is a notable choice. If we evaluate on the 'achievement experience' index, Minh Tai is the clear leader. He has three HBSF Tour titles. He has a national championship. He won a set-format final, with one deficit, and with a deciding overtime set. That is the type of match where experience often plays a large role.

But I do not have data on the ages of these players. I do not have data on Minh Tai's career curve. I do not have head-to-head data between Minh Tai and Lim Leng before this match. With such a small sample, my conclusions only carry value within the scope of this event. I cannot say Minh Tai is 'at his career peak' or 'on the decline'. I can only say he won this match.

Lim Leng's profile: the under-told story

And here is the data point I consider the most overlooked of the entire event.

Lim Leng has reached the final of three consecutive HBSF Tour stops. Three times. This is a level of consistency very few players achieve on any tour, domestic or international. And in all three of those occasions, he did not win — or at least, the original report states no title.

This is a classic sports profile: the 'bridesmaid' — a player who regularly reaches the final but rarely wins. In data analysis, this profile has an interesting property: it oscillates between two interpretations.

Interpretation one: Lim Leng has a very high skill floor but lacks the decisive factor at the final moment. He reaches finals because he is consistent; he loses finals because he lacks the explosive capacity when needed.

Interpretation two: Lim Leng has a very high floor and a high ceiling too, but faces stronger opponents in those specific finals. Three final appearances could be the mark of a very good player in a tour with one better player.

These two interpretations have different consequences. The first implies Lim Leng needs to improve match mentality or closing skill. The second implies Lim Leng is doing everything right and just missing a little luck or a slightly easier opponent.

I do not have enough data to choose. But I can say one thing with confidence: a record of three consecutive finals deserves more coverage than it received. In sport, we usually reward the winner of the final and remember the loser. But data on sustained presence in the final round is an indicator with higher predictive value than a single title. A player's journey is not an upward arrow, but a scatter plot. And Lim Leng's scatter plot has a cluster of points near the top, but no point at the top.

One small detail: the name 'Lim Leng' has a Sino origin or Sino-Vietnamese origin. This could imply the player is Vietnamese-Chinese or a cross-border entrant in the pool draw. But this is speculation, not data. I mark it as 'needs verification' and exclude it from my conclusions.

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Core (continued): prize economics and tour structure

Prize allocation

In carom: - Champion: 70 million VND (about 2,700 USD) - Runner-up: 30 million VND (about 1,180 USD) - Joint bronze: 10 million VND (about 395 USD) - Best Game and Outstanding Series: 5 million VND (about 197 USD) - Top 8: 5 million VND - Top 16: 2 million VND (about 79 USD)

In pool: - Champion: 60 million VND (about 2,350 USD) - Runner-up: 30 million VND - Joint bronze: 10 million VND - Top 8: 3 million VND (about 118 USD)

Note the distribution ratio. In carom, the champion receives 70 million, joint bronze receives 10 million — a 7:1 ratio. But top 16 receives 2 million, and the champion-to-top-16 ratio is 35:1. This is a prize structure with wide distribution at the base but still a significant prize at the top.

In pool, the champion-to-top-8 ratio is 20:1. This structure is somewhat steeper at the higher tiers compared to carom.

From a tournament economics perspective, what does this structure tell us? It says the organisers want to sustain a tournament where even players who do not go deep have a reason to attend. In a semi-professional domestic tour, this is a sensible choice. The event depends on wide player-base participation, not just on a few stars.

But this structure also says this is a tour that has not yet reached the 'top-heavy' stage of international events. At a major international event, the champion typically takes a much larger share of the total prize fund, and top 16 typically receives a much smaller share or nothing. This difference reflects a difference in market scale, not a difference in organisational quality.

The meaning of a six-day event

Another detail worth noting is the six-day duration. This is a tour running six days with two parallel disciplines. This implies a dense schedule and complex logistics. For the players, six days is a challenge of endurance and focus, especially in carom, where matches can stretch to 47 innings as seen in the semifinal.

I have not seen documentation on how Vietnamese carom players typically maintain form across long tours. This is a data gap. If there is one thing I would like to study more about the HBSF Tour, it is the effect of tour duration on player average across stops. Does average decline toward the end of a tour? Is there a measurable 'fatigue' effect?

But that is a question for the future. In this event, I only have data from one tour, and I will not extrapolate it into a law.

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Contrarian angle: 'World Cup' and the limits of a headline

This is the section I need to state clearly, responsibly.

The original headline calls this event a 'Vietnamese Billiards World Cup'. That is a colloquial label, not a formal tier. There is no evidence in my data that this event is part of the World Cup system of any international federation.

But I do not want to write an article that only criticises a headline. What I want to analyse is why this label has appeal, and what it distorts in how we read the results.

The 'World Cup' label has appeal because it anchors the event to a frame audiences already know. If an event is called a 'World Cup', readers automatically apply a set of expectations about quality, intensity, scale. Those expectations may not be met by the event itself, but they make the event more attractive to read.

This is a form of editorial bias. And it has measurable consequences.

When we read Nhu Le's average of 1.85 in the 'World Cup' frame, we might think it is an astonishing number. When we read average 1.85 in the 'semi-professional domestic tour' frame, we understand that it is a solid number but not the peak of this discipline. At the UMB World Cup level, top player averages typically run from 2.0 upward. Some leading players reach average 2.5 or higher in peak periods.

This does not mean 1.85 is a poor number. 1.85 is a good average. But it is a good average at a specific tier. The trophy is not on the scoreboard, it is in the average table — but the average table only means something when you know where it was measured.

I emphasise this for another reason. If we do not clearly separate tiers, we can inadvertently create a false expectation system for the players themselves. A player who wins a domestic tour with average 1.85 could be expected to compete on par with World Cup players. That is an unfair and unhelpful expectation. Separating tiers clearly is a way of respecting a player's achievement, not diminishing it.

The discipline-separation problem

Another contrarian point concerns the structure of the original report.

The original report grouped the carom and pool finals into a single news item. From an editorial perspective, this is reasonable because it is the same tour. But from an analytical perspective, it creates a trap. Readers may inadvertently compare Nhu Le's carom performance with Minh Tai's pool performance, as if the two could be placed side by side.

This is methodologically incorrect. Carom and pool are two sports fundamentally different in kind:

• Carom has no pockets. Pool has pockets.
• Carom scores by inning, pool scores by rack.
• Carom has no break shot. Pool has a break shot with the rack randomness factor.
• Carom ends at a fixed point target. Pool ends by number of sets or racks.
• Carom has no set structure in this event (50-point format, single match). Pool has a set structure.

Because of these differences, comparative indicators have little meaning. An average of 1.85 in carom has no equivalent in pool. A 27-inning match in carom has no equivalent in pool. A 10-point run in carom has no equivalent in pool.

I say this not to diminish anyone. I say this so the reader understands that if they want to compare, they need to compare within the same discipline. Nhu Le can be compared to Nhat Hoa. Minh Tai can be compared to Lim Leng. Nhu Le should not be compared to Minh Tai.

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Core (extended): what the data still lacks

I want to devote this section to listing what my dataset cannot answer — systematically, as preparation for any deeper future analysis.

Missing age and career-curve data

The report states no ages for any player. Without ages, I cannot place results against a career curve. A 22-year-old first-time champion means something entirely different from a 35-year-old first-time champion. A player winning at 40 in carom can still be at peak — carom is a sport with a much longer career curve than many others. But I lack the data to distinguish.

Missing head-to-head data

I have no head-to-head history between Nhu Le and Nhat Hoa, between Minh Tai and Lim Leng, or between any other pair in this group. This is a major gap. In head-to-head sports, direct matchup is one of the strongest predictive variables. Without it, I cannot assess whether Nhat Hoa lost because the opponent was stronger, or because he had a poor match.

Missing season-form context

I do not have any player's season-long average. I do not know how many tours Nhu Le has played before this, or what his average was at those tours. Tour 2 is a snapshot. A snapshot is not a trend. This is why I am highly cautious with the word 'form' — it implies a sequence, whereas I have a single data point.

Missing technical detail

I have no data on shot count, shot type, or ball position in difficult situations. I have no data on how players defended, or how they chose cushion routes. These are what a deep technical carom analysis needs, and I do not have them. I have the outcome, I do not have the process.

Missing pool variant information

I do not know whether the pool at HBSF Tour 2 was 8-ball, 9-ball, or 10-ball. This is an important gap. Pool variants have entirely different tactical characteristics. 9-ball has a higher luck factor because the break can create unexpected openings. 10-ball has a lower luck factor. 8-ball has higher tactical demands with table control. Not knowing the variant, I cannot assess whether Minh Tai's result reflects skill, luck, or tactics.

Missing overtime set rules

As stated above, I do not fully understand the overtime rule in the pool final. This directly affects how I read the result. I will not conclude on the pool match until I verify this rule.

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Contrarian angle (continued): correlation and causation

There is a temptation any data analyst must guard against, and it is especially strong with a dataset like mine.

That temptation is: to see a correlation, and assign it a causal relation.

For example, I could write: 'Nhu Le won because he had a 10-point run to close.' That is a sentence that sounds right. But it reverses the causal relation. The 10-point run is not the cause of the win. The 10-point run is a part of the win. What happened was: Nhu Le played well in the closing phase, and the run was the result. We cannot say that if he had not had the run, he would have lost — because we do not have a duplicate of the match to compare against.

This is a subtle point I want to spend time explaining. In sports where outcomes are decided by a chain of short events (like a carom run, a scoring streak in pool, a goal in football), we tend to narrate the match by selecting the decisive events. But narrative is not analysis. Narrative selects. Analysis counts.

For example, I could narrate the carom match as follows:

'Nhat Hoa led, but Nhu Le closed the gap and exploded at the end to win.'

This narration selects two events: Nhat Hoa's lead and Nhu Le's run. It ignores most of the match — 27 innings with varying averages, defensive shots, difficult situations. It turns a 27-inning match into two moments. That is not a way of reading data. That is a way of storytelling.

I am not saying storytelling is wrong. I am saying storytelling and analysis are two different things, and a data reader needs to distinguish them.

The same problem appears in the pool match. I could narrate:

'Lim Leng won the opening set, but Minh Tai counter-attacked to win the overtime set.'

This narration implies Minh Tai had stronger mentality or better counter-attacking capacity. But it ignores the details of how each rack unfolded. A player can win a set through a lucky break. A player can lose a set through a small error. Without rack-by-rack detail, I cannot distinguish between skill and luck.

This is why I hold to my principle: never write a causal claim when I have only a correlation. In Nhu Le's case, the correlation is between his average of 1.85 in the final and his victory. But that correlation can be explained in at least three ways:

1. Nhu Le played better in the final and that led to the win.
2. Nhat Hoa played worse in the final and that gave Nhu Le space to play better.
3. The table or playing conditions in the final differed from the semifinal, and that affected both players.

I lack the data to choose between these three. So I do not choose. I present all and let the reader weigh them.

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Takeaway: signals for the Final Tour 2026

The HBSF Final Tour 2026 runs from December 16 to 20, 2026, at Rach Mieu Sports Hall, Ho Chi Minh City. It is the season closer, and it is the next data point I will track.

When I look at the Final Tour, I do not look for who will win. I look for three questions that Tour 2 has raised:

First, will Nhu Le sustain a high average in a big match? If he averages above 1.5 at the Final Tour, that is a signal of a player with the ability to elevate in big matches. If his average returns to the 1.1-1.2 range, the 1.06-to-1.85 reversal at Tour 2 is a local phenomenon, not a rule.

Second, will Nhat Hoa improve his closing ability? If he reaches the final again and loses again, his 'front-running' profile is confirmed by data. If he wins, I must revise my model.

Third, will Lim Leng break the threshold? Four consecutive finals is a notable achievement, but the data question is whether he can convert it into a title. If he reaches a fourth final and wins, that is a complete story. If he reaches a fourth final and loses again, that is a story about limits.

I will have at least two data stops when the Final Tour ends — Tour 2 and the Final Tour. That is still a small sample. But it is the small sample I have, and I will handle it with all the caution it needs.

One thing Tour 2 has told me, and I want to close with it. Nhu Le averaged 1.06 in the semifinal and 1.85 in the final. That data does not tell me who he is as a player. It only tells me what he did in two specific sessions. A player is not the average of one match. A player is the distribution of many matches, and we have only just begun to collect that distribution.

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Data limitations

This analysis rests on a clearly limited dataset. I list the limits here so the reader knows exactly what I dare claim and what I lack the data to claim.

Sample size

The entire carom analysis rests on two matches: one semifinal and one final for each player, four matches total. The entire pool analysis rests on three sets of one final. With this sample size, no conclusions about long-term trends can be drawn. Every conclusion in this article has event scope, not season scope.

Confidence interval

With two matches, the average estimate carries a very wide confidence interval. If Nhu Le's true average in any match is 1.4 with a standard deviation of 0.3, then observing 1.06 in one match and 1.85 in another is fully within random prediction. In other words, the 1.06-to-1.85 reversal does not necessarily reflect a real change in form. It could simply be the normal variance of a player averaging 1.4.

Warning against over-extrapolation

I specifically warn against two types of extrapolation:

Tournament extrapolation: Results from HBSF Tour 2 should not be compared to any international event. The tier, prize fund, and tour structure differ fundamentally. Comparisons only make sense within the same system.

Player extrapolation: A player should not be judged on one tour. Nhu Le may win again or never win again. Nhat Hoa may break his profile or confirm it. Lim Leng may win the next final or not. There is not enough data to predict.

Data source

The data in this article comes from the reporting of HBSF Tour 2 MIN Table Cup 2026, published on September 12, 2026. I recalculated averages from the points and innings stated in the report. Calculations may carry rounding error.

Cross-verification

I attempted to cross-verify data against at least one independent source for every important figure. For average figures, I recalculated from two different inputs (points and innings). For prize figures, I cross-checked between the carom and pool sections to confirm consistency. For the story of Lim Leng reaching three consecutive finals, I have only one source, and I therefore mark it with medium confidence.

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I have followed billiards for more than ten years, from domestic tours in Vietnam to international events in the UK and Europe. What I have learned from live tracking is this: results are noise, process is signal. The carom final between Nhu Le and Nhat Hoa was a fine match, and the 50-37 result is a memorable one. But its true value lies in the numbers we can extract from it. Nhu Le averaged 1.85. Nhat Hoa averaged 1.37. Nhu Le averaged 1.06 one session earlier. These three numbers are data. The championship story is the label we place on them.

I will wait for the Final Tour for more data. Until then, I will let the two numbers 1.06 and 1.85 stand side by side, and let you read them.