Channel
@ybtytd
On this record: Topic · Growth · Engagement · Reactions · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Cite this entry
141,651subscribers
+33,133 since we began measuring on 14 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
| Telegram ID | -1001718222921 |
|---|---|
| Type | Channel |
| Username | @ybtytd |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 14 August 2026 |
| Last confirmed live | 11 September 2026 |
| Measurements held | 24 |
| Confirmed unchanged | 1 time, most recently 11 September 2026 |
| On Telegram | t.me/ybtytd |
Gambling & betting — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 14 August 2026 and assigned it the closest of 31 fixed categories, at 99% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Sept 2026, 19:37 | 141,651 | +2,741 |
| 9 Sept 2026, 10:01 | 138,910 | +1,532 |
| 6 Sept 2026, 02:15 | 137,378 | +1,980 |
| 4 Sept 2026, 00:35 | 135,398 | +2,452 |
| 2 Sept 2026, 12:54 | 132,946 | +2,432 |
| 1 Sept 2026, 10:24 | 130,514 | +1,690 |
| 31 Aug 2026, 12:15 | 128,824 | +3,358 |
| 30 Aug 2026, 14:09 | 125,466 | +1,337 |
| 29 Aug 2026, 11:58 | 124,129 | +3,223 |
| 28 Aug 2026, 12:54 | 120,906 | +2,410 |
| 27 Aug 2026, 12:03 | 118,496 | +547 |
| 26 Aug 2026, 15:17 | 117,949 | +1,813 |
| 25 Aug 2026, 14:02 | 116,136 | +2,306 |
| 24 Aug 2026, 15:12 | 113,830 | +1,956 |
| 22 Aug 2026, 22:35 | 111,874 | +1,570 |
| 21 Aug 2026, 13:44 | 110,304 | +2,413 |
| 20 Aug 2026, 14:08 | 107,891 | -489 |
| 19 Aug 2026, 12:23 | 108,380 | -345 |
| 18 Aug 2026, 15:13 | 108,725 | -374 |
| 17 Aug 2026, 18:46 | 109,099 | first reading |
328 posts held, back to 13 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 56 pages of Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 58 of 328 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 11 September 2026 |
|---|---|
| Posts held | 328 (13 August 2026 – 11 September 2026) |
| Views total | 888,164 |
| Reactions total | 45 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Sept 2026, 16:59 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
45 reactions across 42 posts, in 9 distinct kinds. The most used accounts for 71.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 32 | 71.1% | |
| 👏 | 3 | 6.67% | |
| 🎉 | 2 | 4.44% | |
| 🔥 | 2 | 4.44% | |
| 😁 | 2 | 4.44% | |
| ⚡ | 1 | 2.22% | |
| 👍 | 1 | 2.22% | |
| 🤩 | 1 | 2.22% | |
| 🥰 | 1 | 2.22% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 58 of the 328 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 45 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 328 most recent posts we hold, published 13 August 2026 to 11 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
0⃣【开云-9月11日 0点45分 欧冠 PSV埃因霍温 vs 顿涅茨克矿工】 0⃣【开云-9月11日 0点45分 欧冠 费内巴切 vs 罗马】 0⃣【开云-9月11日 3点整 欧冠 拜仁慕尼黑 vs 博多格林特】 0⃣【开云-9月11日 3点整 欧冠 布拉格斯拉维亚 vs 朗斯】 0⃣【开云-9月11日 3点整 欧冠 科莫 vs 莱比锡红牛】 0⃣【开云-9月11日 3点整 欧冠 曼联 vs 沙巴巴库】 0⃣【开云-9月11日 3点15分 葡超 阿马多拉 vs 布拉加】 0⃣【开云-9月11日 8点30分 自由杯 约森独立队 vs 弗拉门戈】 0⃣【开云-9月12日 2点45分 法甲 雷恩 vs 马赛】 0⃣【开云-9月12日 17点整 LPL AL vs IG】 0⃣【开云-9月12日 17点整 日职J1联 水户蜀葵 vs 川崎前锋】 0⃣【开云-9月12日 17点30分 日职J1联 清水心跳 vs 福冈黄蜂】 0⃣【开云-…
0⃣【开云-9月13日 23点30分 英超 曼联 vs 曼城】 （本场赛事红包雨🧧敬请周知！） 数据初盘开出客队-0/0.5，说明机构对曼城实力的认可。曼联前3轮联赛1胜1平1负积4分暂列第11位。球队进攻端具备不错火力，但后防协同性与专注度不足，防线默契度性仍需磨合。曼城3轮联赛全胜积9分领跑英超，且刚在欧冠客场2-0击败波尔图，各项赛事迎来4连胜。球队攻防一体，整体控场能力与高压压制极其强劲。近10次同城德比各有胜负，平分秋色。综合分析，曼城当前连战连捷、攻防统治力极强，客场依然有能力占据主动。本场看好曼城客场取胜。 全场让球：曼城-0/[email protected] 全场大小：大[email protected] 推荐比分：2-3
0⃣【开云-9月13日 23点30分 德甲 艾禾斯堡 vs 拜仁慕尼黑】 数据初盘开出全场让球：艾禾斯堡+2.5，机构倾向客队优势。德国甲级联赛第3轮，主队新赛季两连胜联赛排名第4，球队已斩获四连胜，前场把握进球能力较强，近期表现出色，士气大振。客队上一轮0-0被沙尔克04逼平，攻击力有所下降，表现欠稳，状态回落。两队近年无交锋记录，新赛季整体发挥艾禾斯堡更为出色，另加主场优势，综合分析本场看好主队可以守住指数。 全场让球：艾禾斯堡[email protected] 全场大小：小[email protected] 推荐比分：1-1
0⃣【开云-9月13日 21点30分 德甲 莱比锡红牛 vs 汉堡】 数据初盘开出全场让球：莱比锡红牛-1.5，机构倾向主队优势。德国甲级联赛第3轮，主队近八场主场获得连胜，主场优势明显，球队传控能力较强，阵容完整，整体表现相对稳定。客队新赛季遭受两连败，球队防守能力下降明显，表现甚差，状态低迷。两队近四场交锋莱比锡红牛均获胜，近年整体实力相对更强，另加主场优势，综合分析本场看好主队获胜。 全场让球：莱比锡红牛[email protected] 全场大小：小[email protected] 推荐比分：2-0
0⃣【开云-9月13日 15点30分 韩K1联 金泉尚武 vs 江原FC】 数据方面机构对于两队支持力度均不明显，盘口仅仅开出双方平手盘方向。金泉尚武近期表现较为平淡，连续多场比赛难求一胜，进攻效率有限，防线也存在一定隐患。反观江原FC近期状态更为稳定，攻防两端发挥均衡，且双方近几次交锋江原FC占据明显优势，连续保持不败并多次零封对手。此役江原FC无论整体状态还是心理层面都更占上风，客场具备较强抢分能力，本场看好江原FC客场取胜。 全场让球：江原[email protected] 全场大小：小2/[email protected] 推荐比分：0-1
0⃣【开云-9月13日 7点30分 美职联 迈阿密国际 vs 纳什维尔】 数据方面机构对于主队实力给予支持，盘口开出主队让0/0.5球。迈阿密拥有梅西、苏亚雷斯等核心球员，进攻端个人能力突出，近期火力依旧强劲，主场作战更具优势。纳什维尔本赛季表现同样不俗，但球队客场面对强敌时防守压力较大。综合来看，迈阿密主场优势明显，进攻火力更值得高看，本场看好主队取胜。 全场让球：迈阿密国际-0/[email protected] 全场大小：大[email protected] 推荐比分：3-1
0⃣【开云-9月13日 3点整 西甲 皇家马德里 vs 巴列卡诺】 数据初盘开出主队-2.5，说明机构对皇家马德里实力的认可。皇家马德里目前联赛4轮3胜0平1负积7分暂列第3位。球队刚在9月9日的欧冠小组赛主场2-1力克国际米兰，迅速走出上一轮联赛客场0-1不敌贝蒂斯的阴影。伯纳乌主场攻击火力依然强劲。巴列卡诺目前联赛4轮1胜1平2负积4分暂列第14位。球队虽然上一轮主场3-2险胜桑坦德竞技止跌，但客场能力依然不足，防线存在明显漏洞，抗压能力堪忧。两队近3次交锋皇马2胜1平占据绝对优势。综合分析，本场看好皇家马德里坐镇伯纳乌掌控局面大胜破局。 全场让球：皇家马德里[email protected] 全场大小：小3.5/[email protected] 推荐比分：3-0
0⃣【开云-9月13日 2点45分 法甲 欧塞尔 vs 尼斯】 数据方面机构开出双方平手盘，给予两队状态的支持。欧塞尔近10场4胜2平4负，整体表现尚可，主场具备一定抗衡能力。尼斯近10场3胜4平3负，近期状态同样不算突出，进攻稳定性仍有提升空间。双方上次交手欧塞尔主场2-1取胜，心理层面占据优势。本场欧塞尔坐镇主场，面对状态接近的尼斯有望延续交锋优势，主胜方向值得关注。 全场让球：欧塞尔[email protected] 全场大小：大[email protected] 推荐比分：2-1
0⃣【开云-9月13日 2点45分 法甲 勒阿弗尔 vs 昂热】 数据方面机构开出主队让0/0.5，给予状态的支持。勒阿弗尔近10场3胜2平5负，整体发挥一般，不过近期面对里昂能够客场拿到1-1，球队韧性有所体现。昂热近10场5胜2平3负，状态更为稳定，且近期曾客场3-1击败欧塞尔，反击效率值得关注。不过勒阿弗尔坐镇主场，双方近年交锋差距不大，主队具备一定主场优势。结合主场因素及交锋走势，本场倾向勒阿弗尔主胜。 全场让球：勒阿弗尔-0/[email protected] 全场大小：小2/[email protected] 推荐比分：1-0
0⃣【开云-9月13日 0点30分 英超 托特纳姆热刺 vs 埃弗顿】 数据方面机构向主队方向倾斜，盘口开出主队让0.5球。热刺新赛季开局低迷，前三轮联赛仅取得1平2负，至今一球未进，进攻端问题较为突出。反观埃弗顿前三轮保持不败，攻防表现更加稳定，球队整体防守韧性值得肯定。热刺虽然拥有主场优势，但目前锋线迟迟找不到状态，面对防守稳健的埃弗顿恐怕难有作为。综合来看，客队状态更佳，本场看好埃弗顿客场取胜。 全场让球：埃弗顿[email protected] 全场大小：小[email protected] 推荐比分：0-1
0⃣【开云-9月13日 0点30分 西甲 毕尔巴鄂竞技 vs 埃尔切】 数据初盘开出主队-1/1.5，说明机构对毕尔巴鄂竞技实力的认可。前4轮2胜2负积6分升至第10位。球队状态迅速反弹持续走高，近两轮联赛先是客场2-0零封塞尔塔，随后主场3-0完胜强敌马德里竞技，攻防两端秩序井然，主场实力更胜一筹。埃尔切前4轮0胜1平3负积1分陷入降级区第19位。球队防守端近乎崩溃，4场比赛失球高达12粒，近3轮联赛惨遭三连败，客场防守抗压极差。两队近10次交手，毕尔巴鄂竞技5胜3平2负占据优势。综合分析，埃尔切防线失球连连，客场面临强有力的高压逼抢极易崩溃。本场看好毕尔巴鄂竞技主场再创佳绩豪取三连胜。 全场让球：毕尔巴鄂竞技-1/[email protected] 全场大小：大2.5/[email protected] 推荐比分：3-1
0⃣【开云-9月10日 0点45分 欧冠 巴塞罗那 vs 费耶诺德】 0⃣【开云-9月10日 0点45分 欧冠 斯图加特 vs 维京】 0⃣【开云-9月10日 3点整 欧冠 巴黎圣日耳曼 vs 布拉迪斯拉发】 0⃣【开云-9月10日 3点整 欧冠 里斯本竞技 vs 加拉塔萨雷】 0⃣【开云-9月10日 3点整 欧冠 利物浦 vs 马德里竞技】 0⃣【开云-9月10日 3点整 欧冠 那不勒斯 vs 阿森纳】 0⃣【开云-9月10日 3点整 英联杯 切尔西 vs 利兹联】 0⃣【开云-9月10日 6点整 南球杯 桑托斯SP vs 米内罗竞技】 0⃣【开云-9月10日 8点整 美职联 纽约城 vs 新英格兰革命】 0⃣【开云-9月10日 8点30分 美职联 芝加哥火焰 vs 迈阿密国际】 0⃣【开云-9月10日 2点45分 女篮世界杯 意大利(女) vs 澳大利亚(女)】 0⃣【开云-9月10日 17点30分 女篮世界杯 美国(女) …
Showing the 12 most recent of 328 posts we hold for @ybtytd. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
@ybtytd edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 14 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 9 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 11 September 2026 — this entry's latest reading, not the date you are reading this.
“推单体育赛事推荐足球篮球推单” (@ybtytd), 141,651 subscribers as measured 11 September 2026. Telegram Register, tgregister.com/channel/ybtytd.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.