What are the best agile project estimation tools and techniques?

It turns out that, while humans are pretty awful at estimating completion time, we’re actually pretty good at saying how big something is. We’re especially good at comparative sizing (“it’s bigger than that, but smaller than that over there”). If I showed you two penny jars (one more full than the other) and told you the first had 165 pennies, your estimate of the second jar would be much more accurate. You would be estimating the second relative to the first, which is a known quantity. In the same way, I could present a team with a guide story that they had recently completed in a prior sprint, tell them it's 3 story points, and they should be able to estimate additional user stories relative to the first purely based on size comparison.

Story points differ from time-based estimates by focusing on complexity, effort, and risk rather than fixed hours. They allow teams to gauge the intricacies of tasks more accurately, aiding in better planning and task prioritization. Unlike time estimates prone to inaccuracies due to varying complexities, story points offer a relative measure that helps in comparing tasks, prioritizing effectively, and adapting to changing requirements. Their effectiveness lies in improved accuracy, enhanced adaptability, and the ability to manage complexity efficiently, resolving the challenges faced with traditional time-based estimations.

Planning Poker differs from time-based estimates by using consensus-based values to measure task complexity rather than fixed hours. It fosters team collaboration, reducing individual biases and encouraging discussions for more accurate estimations. This approach solves the problem of over-reliance on fixed time estimates, offering a collective, more nuanced view of task complexities. Its effectiveness lies in promoting team engagement, minimizing estimation errors, and aiding in accurate task planning and prioritization, enhancing overall project management and adaptability to varying complexities.

The main advantage of planning poker is that the opinion of each person on the team has equal weight. Each person is able to choose an estimate before they are influenced by numbers from the rest of the team. This can be done in-person with cards or it can be done online using something like a planning poker web app, which is great for teams that are not colocated. The important thing is that each person's opinion is factored into the end result, rather than allowing the loudest or first voice in the room to proceed unchallenged. Some teams will choose to go with the majority and only discuss if evenly split. Others will default to the highest number if there is a fairly even split, while others will always discuss and try for consensus.

T-shirt sizing streamlines estimation by using relative sizes (S, M, L) instead of specific time units. It helps teams quickly compare task complexities without getting bogged down in precise time estimates. This method solves the problem of overly detailed time estimates, allowing for rapid assessment and prioritization. Its effectiveness lies in simplifying the estimation process, facilitating quick comparisons between tasks, and aiding in efficient prioritization, enabling teams to focus on relative complexities rather than fixed time frames, enhancing adaptability and planning in agile projects.

Velocity tracking measures a team's historical work rate, aiding in future estimations. It solves issues related to inaccurate estimations by using past performance data to predict future work capacity. This approach is significant as it helps in better forecasting and planning, improving the team's ability to set realistic goals and allocate work efficiently. By leveraging historical data, it enhances accuracy, enabling teams to adapt better, forecast more realistically, and make informed decisions, ultimately leading to improved project management and successful sprint completion in agile environments.

Let’s say your sprint is two weeks long and your team of 8 people has an average velocity of 35 points. You’re getting 35 points done in 640 hours of work (8 x 80 hours). If we figure out that a feature is going to take about 100 points worth of work start to finish then I know that’s about 6 weeks of work. The team gets very good at consistently sizing stories, and you leverage that to do your release planning. This calculation works because the duration is consistent from sprint to sprint. Forecasting for feature delivery becomes much easier when you leverage relative sizing and an average velocity that's based on real historical data.