China remains a strong environment for unpredictable and competitive football. The Super League is widely recognized for the level of competition it consistently delivers. We focus on relevant data points to help you understand how teams are performing. As the season progresses, trends begin to define team strengths and weaknesses.
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 18 | ||||
| Tianjin Jinmen Tiger Liaoning Tieren 2026-07-12 |
WLL
WLL |
1X |
66.07%
|
|
| Shanghai Port Dalian Yingbo 2026-07-12 |
LWW
WLW |
1 DNB |
84.11%
|
|
| Wuhan Three Towns Henan Songshan Longmen 2026-07-11 |
LLW
LLW |
X2 |
58.25%
|
|
| Zhejiang Professional Qingdao Hainiu 2026-07-11 |
LLL
DWW |
Handicap 2 (0:2) |
82.73%
|
|
| Shanghai Shenhua Beijing Guoan 2026-07-11 |
WWW
WWW |
1X |
53.33%
|
|
| Shenzhen Xinpengcheng Qingdao West Coast 2026-07-11 |
LLW
WWW |
X2 |
98.96%
|
|
| Chengdu Rongcheng Chongqing Tonglianglong 2026-07-11 |
DWW
LWW |
1 |
54.16%
|
|
| Shandong Taishan Yunnan Yukun 2026-07-10 |
LWW
WLW |
Handicap 2 (0:2) |
66.07%
|
|
| Form | Prediction | Result | Confidence % | |
|---|---|---|---|---|
| Round 17 | ||||
| Qingdao Hainiu Chengdu Rongcheng 2026-07-05 |
WW
WW |
2 | 1:1 |
75.49%
|
| Shanghai Shenhua Zhejiang Professional 2026-07-05 |
WW
LL |
1 DNB | 3:2 |
98.45%
|
| Tianjin Jinmen Tiger Shenzhen Xinpengcheng 2026-07-04 |
LL
LW |
X2 | 3:0 |
85.81%
|
| Yunnan Yukun Henan Songshan Longmen 2026-07-03 |
LW
LW |
X2 | 2:1 |
81.76%
|
| Round 15 | ||||
| Qingdao West Coast Shanghai Shenhua 2026-05-30 |
WDWDD
LDLDL |
X2 | 2:2 |
70.86%
|
| Henan Songshan Longmen Zhejiang Professional 2026-05-30 |
WWWLD
LWDDW |
1 DNB | 0:2 |
81.51%
|
| Chengdu Rongcheng Shandong Taishan 2026-05-30 |
LLWWW
DLWWW |
1 DNB | 1:0 |
65.82%
|
| Chongqing Tonglianglong Beijing Guoan 2026-05-30 |
WDLDD
LDWDW |
2:3 |
|
|
| Shenzhen Xinpengcheng Qingdao Hainiu 2026-05-30 |
WDLLD
LLLWW |
X2 | 3:2 |
73.11%
|
| Liaoning Tieren Shanghai Port 2026-05-29 |
WWDLL
DWDDD |
X2 | 3:2 |
70.86%
|
Our methodology
Predictions for the Super League are generated through structured data analysis and modeling techniques. We apply methods including Linear Regression to assess consistency and output. We evaluate performance using a combination of offensive and defensive data. This helps present each match from a data-informed perspective.
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