Free football predictions and tips for Malaysia Super League

14:0011.09
Johor Darul Takzim FC
Negeri Sembilan
1

Best and Worst Teams Wins

Malaysia
  • More Wins
Johor Darul Takzim FC
4
Selangor
3
Kuching FA
3
Sabah FA
2
Kuala Lumpur FA
2
Malaysia
  • Less Wins
Imigresen
0
Kelantan Red Warrior FC
0
Penang
0
DPMM FC Brunei
1
Melaka
1

Best and Worst Teams Draws

Malaysia
  • More Draws
Terengganu
1
Penang
1
Negeri Sembilan
1
Imigresen
1
Selangor
0
Malaysia
  • Less Draws
DPMM FC Brunei
0
Johor Darul Takzim FC
0
Kelantan Red Warrior FC
0
Kuala Lumpur FA
0
Kuching FA
0

Best and Worst Teams Losses

Malaysia
  • More Losses
Kelantan Red Warrior FC
4
Penang
2
Melaka
2
Kuala Lumpur FA
2
Imigresen
2
Malaysia
  • Less Losses
Johor Darul Takzim FC
0
Selangor
0
Kuching FA
1
Negeri Sembilan
1
Sabah FA
1

Best and Worst Attacking Teams

Malaysia
  • Best Attacking Teams
Johor Darul Takzim FC
22
Kuching FA
13
Selangor
10
Negeri Sembilan
6
Sabah FA
5
Malaysia
  • Worst Attacking Teams
Penang
0
DPMM FC Brunei
1
Kelantan Red Warrior FC
2
Imigresen
3
Terengganu
3

Best and Worst Defending Teams

Malaysia
  • Best Defending Teams
Johor Darul Takzim FC
0
Kuching FA
3
Penang
3
Sabah FA
3
Selangor
3
Malaysia
  • Worst Defending Teams
Kelantan Red Warrior FC
21
Imigresen
12
Kuala Lumpur FA
10
Melaka
7
Negeri Sembilan
4

Malaysia Super League Last Results and Predictions

Malaysia Super League Standings

# Name P W D L Goals Last 5 Pts
1
Johor Darul Takzim FC
4 4 0 0 22:0
WWWW
12
2
Kuching FA
4 3 0 1 13:3
WWWL
9
3
Selangor
3 3 0 0 10:3
WWW
9
4
Sabah FA
3 2 0 1 5:3
LWW
6
5
Kuala Lumpur FA
4 2 0 2 4:10
WWLL
6
6
Negeri Sembilan
3 1 1 1 6:4
DLW
4
7
Terengganu
3 1 1 1 3:3
DLW
4
8
Melaka
3 1 0 2 4:7
LWL
3
9
DPMM FC Brunei
3 1 0 2 1:4
LLW
3
10
Penang
3 0 1 2 0:3
DLL
1
11
Imigresen
3 0 1 2 3:12
DLL
1
12
Kelantan Red Warrior FC
4 0 0 4 2:21
LLLL
0

When are the Malaysia Super League Predictions available?

You can track down our Super League tips and predictions on this page, predictions are posted 4 days before any Super League event. Using artificial intelligence on football predictions help users predict outcomes for a event.

The goal of our Malaysia Super League Predictions?

The objective is give most-precise forecast while being totally straightforward in doing as such. We firmly trust that having the right tool here in our daily football predictions the expectations can have a massive effect between Super League fans and punters.
  • Top Countries
  • News and Articles
Machine learning vs human intuition in match forecasting

Machine learning vs human intuition in match forecasting

Compare machine learning and human intuition in match forecasting, examining strengths, blind spots, and why a hybrid approach often delivers the most dependable predictions.

Value betting explained with examples from AI probabilities

Value betting explained with examples from AI probabilities

A clear, example-driven guide to value betting using AI probabilities, showing how to convert odds into implied probabilities, spot positive expected value, size stakes responsibly, and avoid common traps.

How AI Is Changing Football Predictions in 2026

How AI Is Changing Football Predictions in 2026

Explore how artificial intelligence is transforming football predictions through richer data, smarter models, real-time context, and stronger transparency and governance.

Using Football Predictions AI to Avoid Bad Bets, Not Chase Winners

Using Football Predictions AI to Avoid Bad Bets, Not Chase Winners

Use Football Predictions AI as a disciplined filter to avoid low-edge bets, manage risk, and make probability-based decisions instead of chasing winners.

The best data sources for AI soccer predictions

The best data sources for AI soccer predictions

A practical guide to the best data sources for AI soccer predictions, covering event data, tracking data, odds, injuries, and open datasets, plus how to choose, validate, and combine them.

Over and under goals: how AI predicts goal totals

Over and under goals: how AI predicts goal totals

Learn how AI predicts over and under goals by estimating expected goals, tempo, team styles, and variance, then converting them into calibrated probabilities for total goals markets.

Odd:1.38